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OpenNeuro48/100

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →

zenodo48/100

Histological Dataset for Microvascular Segmentation of Tissue-Engineered Vascular Grafts

Objectives: The pursuit of understanding vascular tissue regeneration within tissue-engineered vascular grafts (TEVGs) is of paramount importance due to the critical role these grafts play in replacing damaged or diseased blood vessels. TEVGs offer a promising alternative to traditional grafts, with the potential to integrate into the host's tissue and support the natural regenerative processes. However, challenges such as thrombosis, inflammation, and the need for grafts that can adapt to the dynamic biological environment remain. By studying the regenerative processes in TEVGs, researchers can gain insights into the mechanisms that underpin successful graft integration and function, which is essential for improving patient outcomes in vascular surgeries. This dataset, with its detailed annotations of histological features, provides a valuable resource for developing and refining machine-learning models that can analyze and predict patterns of vascular tissue regeneration. The ability to accurately segment and quantify microvessels and immune cells in regenerated arteries is a significant step forward in distinguishing between physiological and pathological regeneration, ultimately contributing to the design of more effective and reliable TEVGs for clinical use. Ethical Approval: Experimental strategy of the study is described in detail in <a href="https://www.mdpi.com/2073-4360/14/23/5149" target="_blank" rel="noopener">[1]</a> and <a href="https://www.mdpi.com/1422-0067/24/10/8540" target="_blank" rel="noopener">[2]</a>. The study was conducted according to the guidelines of the Declaration of Helsinki, and was approved by the Local Ethical Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russia, protocol code 2020/06, date of approval: 19 February 2020). Animal experiments were performed in accordance with the European Convention for the Protection of Vertebrate Animals (Strasbourg, 1986) and Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes. For the implantation, we used female Edilbay sheep of 42–45 kg body weight which were received from the Animal Core Facility of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russia) and selected for the surgery by Doppler ultrasonography to identify those having carotid artery diameter of 4.0 ± 0.2 mm. Description: The dataset comprises a collection of Whole Slide Images (WSIs) obtained from biodegradable TEVGs implanted into the carotid arteries of 20 sheep. A total of 104 WSIs were acquired, each measuring an average size of 135,000 x 123,000 pixels. These WSIs were stained using Hematoxylin and Eosin (H&E), a common practice for highlighting the structure of tissue sections, which facilitates the detailed examination of histological features. These WSIs were automatically sliced into 99,831 patches of 3,000 x 3,000 pixels and subsequently filtered, resulting in 1,401 selected patches for manual annotation. Annotation Method: Two pathologists independently selected and meticulously annotated the 1401 patches, identifying nine distinct histological features associated with vascular tissue regeneration. These features include arteriole lumen (AL), arteriole media (AM), arteriole adventitia (AA), venule lumen (VL), venule wall (VW), capillary lumen (CL), capillary wall (CW), immune cells (IC), and nerve trunks (NT). The annotations were performed using binary masks, delineating each feature within the patches. Subsequently, a senior pathologist conducted a triple verification process, reviewing and refining the annotations to ensure accuracy and consistency. The annotations are provided in the form of binary masks, meticulously defined for each feature within the patches. Dataset Split: Given the limited number of subjects studied, comprising 20 sheep, we employed a 5-fold cross-validation technique to split our dataset. This method was chosen because it allows for the efficient use of limited data, ensuring that each observation has the opportunity to be used in both the training and testing sets, thus reducing bias and providing a more accurate estimate of the model's performance. In this approach, each fold involved 16 sheep for training and the remaining 4 for testing (see Table 1 and Figure 3). This partitioning scheme was consistently applied to maintain the integrity of subject groups within each subset and to prevent data leakage. The 5-fold cross-validation is particularly beneficial for our study's objectives as it maximizes the training data available for developing robust machine learning models while also ensuring that the models are tested on unseen data, thereby enhancing the generalizability of our findings. Access to the Study: Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories: <ul> <li>Source code: <a href="https://github.com/ViacheslavDanilov/histology_segmentation" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/histology_segmentation</a></li> <li>Dataset: <a href="https://doi.org/10.5281/zenodo.10838384" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10838384</a></li> <li>Models: <a href="https://doi.org/10.5281/zenodo.10838431" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10838431</a></li> </ul> <div> </div> <div>Table 1. Patch and feature distributions across folds and subsets <table> <tbody> <tr> <td> Fold </td> <td> Subset </td> <td> Patches </td> <td> AL </td> <td> AM </td> <td> AA </td> <td> VL </td> <td> VW </td> <td> CL </td> <td> CW </td> <td> IC </td> <td> NT </td> <td> Total </td> </tr> <tr> <td> 1 </td> <td> Train </td> <td> 1168 </td> <td> 510 </td> <td> 512 </td> <td> 220 </td> <td> 675 </td> <td> 648 </td> <td> 770 </td> <td> 765 </td> <td> 409 </td> <td> 448 </td> <td> 4957 </td> </tr> <tr> <td>1</td> <td> Test </td> <td> 233 </td> <td> 81 </td> <td> 84 </td> <td> 36 </td> <td> 186 </td> <td> 169 </td> <td> 178 </td> <td> 182 </td> <td> 91 </td> <td> 25 </td> <td> 1032 </td> </tr> <tr> <td> 2 </td> <td> Train </td> <td> 1053 </td> <td> 406 </td> <td> 411 </td> <td> 179 </td> <td> 678 </td> <td> 638 </td> <td> 743 </td> <td> 746 </td> <td> 423 </td> <td> 315 </td> <td> 4539 </td> </tr> <tr> <td>2</td> <td> Test </td> <td> 348 </td> <td> 185 </td> <td> 185 </td> <td> 77 </td> <td> 183 </td> <td> 179 </td> <td> 205 </td> <td> 201 </td> <td> 77 </td> <td> 158 </td> <td> 1450 </td> </tr> <tr> <td> 3 </td> <td> Train </td> <td> 1127 </td> <td> 507 </td> <td> 511 </td> <td> 222 </td> <td> 743 </td> <td> 702 </td> <td> 759 </td> <td> 760 </td> <td> 299 </td> <td> 423 </td> <td> 4926 </td> </tr> <tr> <td>3</td> <td> Test </td> <td> 274 </td> <td> 84 </td> <td> 85 </td> <td> 34 </td> <td> 118 </td> <td> 115 </td> <td> 189 </td> <td> 187 </td> <td> 201 </td> <td> 50 </td> <td> 1063 </td> </tr> <tr> <td> 4 </td> <td> Train </td> <td> 1064 </td> <td> 466 </td> <td> 472 </td> <td> 199 </td> <td> 611 </td> <td> 566 </td> <td> 759 </td> <td> 758 </td> <td> 423 </td> <td> 291 </td> <td> 4545 </td> </tr> <tr> <td>4</td> <td> Test </td> <td> 337 </td> <td> 125 </td> <td> 124 </td> <td> 57 </td> <td> 250 </td> <td> 251 </td> <td> 189 </td> <td> 189 </td> <td> 77 </td> <td> 182 </td> <td> 1444 </td> </tr> <tr> <td> 5 </td> <td> Train </td> <td> 1192 </td> <td> 475 </td> <td> 478 </td> <td> 204 </td> <td> 737 </td> <td> 714 </td> <td> 761 </td> <td> 759 </td> <td> 446 </td> <td> 415 </td> <td> 4989 </td> </tr> <tr> <td>5</td> <td> Test </td> <td> 209 </td> <td> 116 </td> <td> 118 </td> <td> 52 </td> <td> 124 </td> <td> 103 </td> <td> 187 </td> <td> 188 </td> <td> 54 </td> <td> 58 </td> <td> 1000 </td> </tr> </tbody> </table> </div>

opencc-by-4.0Mar 2024View details →

zenodo48/100

Neurodevelopmental Patterns of Early Postnatal White Matter Maturation Represent Distinct Underlying Microstructure and Histology

This dataset includes: <ol> <li>T2w Template from the dHCP datasets.</li> <li>4D weekly average maps from the dHCP study: i) average T2w; ii) average T2w/T1w signal ratio; iii) average neurite density index [from NODDI]; iv) average free water map [from NODDI].</li> <li>NMF results - NeWMaPs from the dHCP study across multiple resolutions (ranging from 2-20 NMFs).</li> </ol>

opencc-by-4.0Jun 2022View details →

zenodo48/100

DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours

Latest description of this data set: <a href="https://github.com/MRC-Harwell/cytometer/blob/main/DATA.md">Data.md at cytometer project</a> <pre># Publications related to the data The data associated to the DeepCytometer project (https://github.com/MRC-Harwell/cytometer) is available from Zenodo (doi: 10.5281/zenodo.5137433 and 10.5281/zenodo.5149005). The histology and mouse measures were generated as part of the Small et al. 2018 study: > Small et al. "Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition". Nature Genetics, 50:572–580, 2018. The hand traced data set, colour maps, and automatic segmentations were generated for the Casero et al. 2021 paper: > Casero et al. "Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks". bioRxiv, 2021. doi: [10.1101/2021.06.03.444997](https://www.biorxiv.org/content/10.1101/2021.06.03.444997v1.full). # Data protocols ## Histology and laboratory measures To develop and evaluate our methods we used Klf14tm1(KOMP)Vlcg C57BL/6NTac (B6NTac) mice tissue samples and additional data generated as part of the Small et al. 2018 study(Small et al. 2018). It should be noted that the single exon Klf14 gene is imprinted and only expressed from the maternally inherited allele(Parker-Katiraee et al. 2007). This was taken into account by (Small et al. 2018) by crossing a Het parent with a WT parent, so that each offspring inherited a WT allele from the WT parent, and the Klf14 gene knockout or a WT allele from the other parent (from the father, PAT, or the mother, MAT). We also take Klf14 imprinting into account by using as controls the PAT mice and comparing them to the MAT WT and MAT Het (or functional KO, FKO) mice.  We used a total of 76 Klf14-B6NTac mice (nfemale=nmale=38), of which 20 mice from the Control and FKO groups were used for training and testing the DeepCytometer pipeline, as well as the hand traced population experiment (summary in Table MICE). The histopathology screen involved fixing, processing and embedding in wax, sectioning and staining with Hematoxylin and Eosin (H&E) both inguinal subcutaneous and gonadal adipose depots. For paraffin-embedded sections, all samples were fixed in 10% neutral buffered formalin (Surgipath) for at least 48 hours at RT and processed using an Excelsior™ AS Tissue Processor (Thermo Scientific). Samples were embedded in molten paraffin wax and 8 μm sections were cut through the respective depots using a Finesse™ ME+ microtome (Thermo Scientific). Sampling was conducted at 2sxns per slide, 3 slides per depot block onto simultaneous charged slides, stained with haematoxylin Gill 3 and eosin (Thermo scientific) and scanned using an NDP NanoZoomer Digital pathology scanner (RS C10730 Series; Hamamatsu). Body weight (BW) and depot weight (DW) were measured with Satorius BAL7000 scales. ## White adipose tissue segmentation For cell area quantification, we applied DeepCytometer v8 to 75 inguinal subcutaneous and 72 gonadal whole histology slides with DeepCytometer (with the Corrected method), including the 20 slides sampled for the hand-traced data set, corresponding to 73 females and 74 males, to produce 2,560,067 subcutaneous and 2,467,686 gonadal cells (on average, 34,134 and 34,273 cells per slide, respectively). Full segmentation of all whole slides was performed with script [klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py). In this case, the segmentation contours were grouped by tiles in the output AIDA annotation `.json` file (one contour per cell, one file per slide). Non-white adipocyte contours were filtered out, and white adipocyte contours were aggregated into an AIDA annotation `.json` file with a single tile with script [klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py) (one contour per cell, one file per slide). # List of directories and files ## Casero et al. (2021) "DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours" (doi: 10.5281/zenodo.5137433) ### `deepcytometer_pipeline_v8.zip` (60.6 MB) Weights, colourmaps, etc. necessary to run the pipeline (v8, with mode colour correction). This is the version of the pipeline described in the paper. There are 10 weight files per convolutional neural network (CNN), corresponding to 10-fold cross-validation * `klf14_b6ntac_exp_0086_cnn_dmap_model_fold_[0..9].h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0089_cnn_segmentation_correction_overlapping_scaled_contours_model_fold_[0..9].h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0091_cnn_contour_after_dmap_model_fold_[0..9].h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0095_cnn_tissue_classifier_fcn_model_fold_[0..9].h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) * `klf14_b6ntac_exp_0094_generate_extra_training_images.pickle`: training dataset description * **'file_list'**: list of SVG files with hand-traced contours for network training. Each SVG file has a corresponding TIFF file with the histology used for segmentation * **'idx_test'**: 10 lists with file indices for testing in 10-fold cross-validation * **'idx_train'**: 10 lists with file indices for training in 10-fold cross-validation * **'fold_seed'**: seed number used for the random number generator to assign file indices to folds * `klf14_b6ntac_exp_0098_filename_area2quantile.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0098_full_slide_size_analysis_v7.py` using the whole Klf14 data set with v7 of the pipeline, and used in earlier experiments, including some where v8 of the pipeline was used for segmentation. * `klf14_b6ntac_exp_0106_filename_area2quantile_v8.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py` using the whole Klf14 data set with v8 of the pipeline, and used in later experiments. * `klf14_training_colour_histogram.npz`: statistics from Klf14 histology images to be used in colour correction * **'xbins_edge'**, **'xbins'**: edges and centres of the bins used for histogram calculations * **'hist_r_q1'**, **'hist_r_q2'**, **'hist_r_q3'** * **'hist_g_q1'**, **'hist_g_q2'**, **'hist_g_q3'** * **'hist_b_q1'**, **'hist_b_q2'**, **'hist_b_q3'**: density quartiles (Q1, Q2, Q3) for RGB channels for each bin the histogram * **'mode_r'**, **'mode_g'**, **'mode_b'**: modes for RGB channels (this corresponds to the most typical background colour in the histology images) * **'mean_l'**, **'mean_a'**, **'mean_b'**: mean intensity for L*a*b channels of the image * **'std_l'**, **'std_a'**, **'std_b'**: intensity standard deviations for L*a*b channels of the image * `klf14_exp_0112_training_colour_histogram.npz`: other statistics from Klf14 histology images to be used in colour correction * **'p'**: vector of quantile values used in ECDF calculations * **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'**: all intensity values for the RGB channels of Klf14 training images that contain at least a white adipocyte * **'f_ecdf_to_val_r_klf14'**, **'f_ecdf_to_val_g_klf14'**, **'f_ecdf_to_val_b_klf14'**: linear interpolation function that maps ECDF quantiles to intensity values in the Klf14 training data set. These functions can be used together with intensity->quantile interpolation functions calculated for a new histology image to perform histogram matching colour correction * **'mean_klf14'**, **'std_klf14'**: mean and standard deviation of the **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'** vectors There are also weight files for the pipeline trained with all the data, instead of the 10-fold cross-validation partition. These were not used for the paper, but could be useful for future experiments * `klf14_b6ntac_exp_0101_cnn_dmap_model.h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0104_cnn_segmentation_correction_overlapping_scaled_contours_model.h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0102_cnn_contour_after_dmap_model.h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0103_cnn_tissue_classifier_fcn_model.h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) ### `histology.7z` (29.1 GB) 165 H&E histology whole slides from Hamamatsu scanner (`.ndpi`). ### `klf14.7z` (2.3 GB) Mice metadata, training/testing data sets for the pipeline, intermediate files created during training, and neural network weights for multiple experiments. * `klf14_b6ntac_meta_info.csv`: Klf14 mice metadata * **Animal Identifier**, **id:** unique ID for each mouse * **ko_parent:** heterozygous parent of origin for the KO allele (father, PAT or mother, MAT) * **sex:** female or male * **genotype:** wild type (KLF14-KO:WT) or heterozygous (KLF14-KO:Het) * **BW:** body weight (g) * **SC:** subcutaneous depot weight (g) * **gWAT:** gonadal depot weight (g) * **Liver:** livel weight (g) * **cull_age:** age at time of culling (days) * **BW_alive:** body weight measured before culling * **BW_alive_date:** age at time of BW_alive measure * **mother:** unique ID for mouse's mother * **mother_genotype:** mouse's mother genotype * `klf14_b6ntac_training`: Directory with hand-traced segmentations of training histology windows. 131 windows sampled from 20 whole slides, plus hand-traced contours that were used for training DeepCytometer and compute population distributions. These segmentations were used for CNN training, but note that there's a cleaned-up version of these data below, and it was the cleaned-up version that was used for the paper experiments * `ndpifile_row_YYYYYY_col_XXXXXX[.tif/.xcf/.svg]`: * **ndpifile:** name of the whole slide file (e.g. `KLF14-B6NTAC 36.1c PAT 98-16 C1 - 2016-02-11 10.45.00`) * **row_YYYYYY:** Y-coordinate of the top-left corner of the sampling window, in pixels * **col_XXXXXX:** X-coordinate of the top-left corner of the sampling window, in pixels * **.tif:** TIFF file with the histology sampling window * **.xcf:** Gimp file with the histology and hand-traced contours (the contours were drawn in Gimp) * **.svg:** SVG (Scalable Vector Graphics) that contains the hand-traced contours in the XCF file * `klf14_b6ntac_training_v2`: Same as `klf14_b6ntac_training`, but the hand-traced data set was cleaned up to remove small contours of dubious cells, or cells that are fully overlapped by others * `klf14_b6ntac_training_non_overlap`: Directory with intermediate images to train the networks. These images are generated by script [`klf14_b6ntac_training_non_overlap`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0077_generate_non_overlap_training_images.py) * `klf14_b6ntac_training_augmented`: Directory with intermediate images used to train the networks (using augmentation to reduce overfitting). These images are generated by script [`klf14_b6ntac_exp_0078_generate_augmented_training_images.py`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0078_generate_augmented_training_images.py) * `klf14_b6ntac_seg`: Deprecated. Directory to store whole slide coarse segmentations in old experiments (e.g. `klf14_b6ntac_exp_0076_generate_training_images.py`). Of little interest for most users * `klf14_b6ntac_results`: Deprecated. Directory to store miscellanea output from some experiments. Of little interest for most users ## Casero et al. (2021). "Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations" (doi: 10.5281/zenodo.5149005) ### `aida_data_Klf14_v8_images.7z` (16.9 GB) Histology images converted to DeepZoom so that they can be visualised with [AIDA](https://github.com/alanaberdeen/AIDA). To use this, decompress this file and put the resulting `images` directory in your `AIDA/dist/data/` directory. ### `aida_data_Klf14_v8_annotations.7z` (18 GB) White adipocyte segmentations in AIDA annotation `.json` files (one contour per cell, one file per whole slide). Each slide has the following files: * `SLIDENAME.json`: Soft link to the annotations file that we want to associate to slide `SLIDENAME.ndpi`, e.g. `SLIDENAME` = `KLF14-B6NTAC-PAT-39.2d 454-16 B1 - 2016-03-17 12.16.06` * `SLIDENAME.lock`: Empty file used to tell the pipeline that `SLIDENAME.ndpi` has already been processed or is being currently processed * `SLIDENAME_coarse_mask.npz`: File with the coarse tissue segmentation of `SLIDENAME.ndpi` and the internal state of the pipeline (execution times, steps, etc) * `SLIDENAME_exp_0106_auto.json`: Annotations (all segmentations without filtering from the Auto algorithm, i.e. segmentation without object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_auto_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Auto algorithm. All contours aggregated into a single tile * `SLIDENAME_exp_0106_corrected.json`: Annotations (all segmentations without filtering from the Corrected algorithm, i.e. segmentation with object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_corrected_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Corrected algorithm. All contours aggregated into a single tile To use this, decompress this file and put the resulting `annotations` directory in your `AIDA/dist/data/` directory. </pre>

opencc-by-4.0Jul 2021View details →

zenodo48/100

Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations

Latest description of this data set: <a href="https://github.com/MRC-Harwell/cytometer/blob/main/DATA.md">Data.md at cytometer project</a> <pre># Publications related to the data The data associated to the DeepCytometer project (https://github.com/MRC-Harwell/cytometer) is available from Zenodo (doi: 10.5281/zenodo.5137433 and 10.5281/zenodo.5149005). The histology and mouse measures were generated as part of the Small et al. 2018 study: > Small et al. "Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition". Nature Genetics, 50:572–580, 2018. The hand traced data set, colour maps, and automatic segmentations were generated for the Casero et al. 2021 paper: > Casero et al. "Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks". bioRxiv, 2021. doi: [10.1101/2021.06.03.444997](https://www.biorxiv.org/content/10.1101/2021.06.03.444997v1.full). # Data protocols ## Histology and laboratory measures To develop and evaluate our methods we used Klf14tm1(KOMP)Vlcg C57BL/6NTac (B6NTac) mice tissue samples and additional data generated as part of the Small et al. 2018 study(Small et al. 2018). It should be noted that the single exon Klf14 gene is imprinted and only expressed from the maternally inherited allele(Parker-Katiraee et al. 2007). This was taken into account by (Small et al. 2018) by crossing a Het parent with a WT parent, so that each offspring inherited a WT allele from the WT parent, and the Klf14 gene knockout or a WT allele from the other parent (from the father, PAT, or the mother, MAT). We also take Klf14 imprinting into account by using as controls the PAT mice and comparing them to the MAT WT and MAT Het (or functional KO, FKO) mice.  We used a total of 76 Klf14-B6NTac mice (nfemale=nmale=38), of which 20 mice from the Control and FKO groups were used for training and testing the DeepCytometer pipeline, as well as the hand traced population experiment (summary in Table MICE). The histopathology screen involved fixing, processing and embedding in wax, sectioning and staining with Hematoxylin and Eosin (H&E) both inguinal subcutaneous and gonadal adipose depots. For paraffin-embedded sections, all samples were fixed in 10% neutral buffered formalin (Surgipath) for at least 48 hours at RT and processed using an Excelsior™ AS Tissue Processor (Thermo Scientific). Samples were embedded in molten paraffin wax and 8 μm sections were cut through the respective depots using a Finesse™ ME+ microtome (Thermo Scientific). Sampling was conducted at 2sxns per slide, 3 slides per depot block onto simultaneous charged slides, stained with haematoxylin Gill 3 and eosin (Thermo scientific) and scanned using an NDP NanoZoomer Digital pathology scanner (RS C10730 Series; Hamamatsu). Body weight (BW) and depot weight (DW) were measured with Satorius BAL7000 scales. ## White adipose tissue segmentation For cell area quantification, we applied DeepCytometer v8 to 75 inguinal subcutaneous and 72 gonadal whole histology slides with DeepCytometer (with the Corrected method), including the 20 slides sampled for the hand-traced data set, corresponding to 73 females and 74 males, to produce 2,560,067 subcutaneous and 2,467,686 gonadal cells (on average, 34,134 and 34,273 cells per slide, respectively). Full segmentation of all whole slides was performed with script [klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py). In this case, the segmentation contours were grouped by tiles in the output AIDA annotation `.json` file (one contour per cell, one file per slide). Non-white adipocyte contours were filtered out, and white adipocyte contours were aggregated into an AIDA annotation `.json` file with a single tile with script [klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py) (one contour per cell, one file per slide). # List of directories and files ## Casero et al. (2021) "DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours" (doi: 10.5281/zenodo.5137433) ### `deepcytometer_pipeline_v8.zip` (60.6 MB) Weights, colourmaps, etc. necessary to run the pipeline (v8, with mode colour correction). This is the version of the pipeline described in the paper. There are 10 weight files per convolutional neural network (CNN), corresponding to 10-fold cross-validation * `klf14_b6ntac_exp_0086_cnn_dmap_model_fold_[0..9].h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0089_cnn_segmentation_correction_overlapping_scaled_contours_model_fold_[0..9].h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0091_cnn_contour_after_dmap_model_fold_[0..9].h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0095_cnn_tissue_classifier_fcn_model_fold_[0..9].h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) * `klf14_b6ntac_exp_0094_generate_extra_training_images.pickle`: training dataset description * **'file_list'**: list of SVG files with hand-traced contours for network training. Each SVG file has a corresponding TIFF file with the histology used for segmentation * **'idx_test'**: 10 lists with file indices for testing in 10-fold cross-validation * **'idx_train'**: 10 lists with file indices for training in 10-fold cross-validation * **'fold_seed'**: seed number used for the random number generator to assign file indices to folds * `klf14_b6ntac_exp_0098_filename_area2quantile.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0098_full_slide_size_analysis_v7.py` using the whole Klf14 data set with v7 of the pipeline, and used in earlier experiments, including some where v8 of the pipeline was used for segmentation. * `klf14_b6ntac_exp_0106_filename_area2quantile_v8.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py` using the whole Klf14 data set with v8 of the pipeline, and used in later experiments. * `klf14_training_colour_histogram.npz`: statistics from Klf14 histology images to be used in colour correction * **'xbins_edge'**, **'xbins'**: edges and centres of the bins used for histogram calculations * **'hist_r_q1'**, **'hist_r_q2'**, **'hist_r_q3'** * **'hist_g_q1'**, **'hist_g_q2'**, **'hist_g_q3'** * **'hist_b_q1'**, **'hist_b_q2'**, **'hist_b_q3'**: density quartiles (Q1, Q2, Q3) for RGB channels for each bin the histogram * **'mode_r'**, **'mode_g'**, **'mode_b'**: modes for RGB channels (this corresponds to the most typical background colour in the histology images) * **'mean_l'**, **'mean_a'**, **'mean_b'**: mean intensity for L*a*b channels of the image * **'std_l'**, **'std_a'**, **'std_b'**: intensity standard deviations for L*a*b channels of the image * `klf14_exp_0112_training_colour_histogram.npz`: other statistics from Klf14 histology images to be used in colour correction * **'p'**: vector of quantile values used in ECDF calculations * **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'**: all intensity values for the RGB channels of Klf14 training images that contain at least a white adipocyte * **'f_ecdf_to_val_r_klf14'**, **'f_ecdf_to_val_g_klf14'**, **'f_ecdf_to_val_b_klf14'**: linear interpolation function that maps ECDF quantiles to intensity values in the Klf14 training data set. These functions can be used together with intensity->quantile interpolation functions calculated for a new histology image to perform histogram matching colour correction * **'mean_klf14'**, **'std_klf14'**: mean and standard deviation of the **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'** vectors There are also weight files for the pipeline trained with all the data, instead of the 10-fold cross-validation partition. These were not used for the paper, but could be useful for future experiments * `klf14_b6ntac_exp_0101_cnn_dmap_model.h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0104_cnn_segmentation_correction_overlapping_scaled_contours_model.h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0102_cnn_contour_after_dmap_model.h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0103_cnn_tissue_classifier_fcn_model.h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) ### `histology.7z` (29.1 GB) 165 H&E histology whole slides from Hamamatsu scanner (`.ndpi`). ### `klf14.7z` (2.3 GB) Mice metadata, training/testing data sets for the pipeline, intermediate files created during training, and neural network weights for multiple experiments. * `klf14_b6ntac_meta_info.csv`: Klf14 mice metadata * **Animal Identifier**, **id:** unique ID for each mouse * **ko_parent:** heterozygous parent of origin for the KO allele (father, PAT or mother, MAT) * **sex:** female or male * **genotype:** wild type (KLF14-KO:WT) or heterozygous (KLF14-KO:Het) * **BW:** body weight (g) * **SC:** subcutaneous depot weight (g) * **gWAT:** gonadal depot weight (g) * **Liver:** livel weight (g) * **cull_age:** age at time of culling (days) * **BW_alive:** body weight measured before culling * **BW_alive_date:** age at time of BW_alive measure * **mother:** unique ID for mouse's mother * **mother_genotype:** mouse's mother genotype * `klf14_b6ntac_training`: Directory with hand-traced segmentations of training histology windows. 131 windows sampled from 20 whole slides, plus hand-traced contours that were used for training DeepCytometer and compute population distributions. These segmentations were used for CNN training, but note that there's a cleaned-up version of these data below, and it was the cleaned-up version that was used for the paper experiments * `ndpifile_row_YYYYYY_col_XXXXXX[.tif/.xcf/.svg]`: * **ndpifile:** name of the whole slide file (e.g. `KLF14-B6NTAC 36.1c PAT 98-16 C1 - 2016-02-11 10.45.00`) * **row_YYYYYY:** Y-coordinate of the top-left corner of the sampling window, in pixels * **col_XXXXXX:** X-coordinate of the top-left corner of the sampling window, in pixels * **.tif:** TIFF file with the histology sampling window * **.xcf:** Gimp file with the histology and hand-traced contours (the contours were drawn in Gimp) * **.svg:** SVG (Scalable Vector Graphics) that contains the hand-traced contours in the XCF file * `klf14_b6ntac_training_v2`: Same as `klf14_b6ntac_training`, but the hand-traced data set was cleaned up to remove small contours of dubious cells, or cells that are fully overlapped by others * `klf14_b6ntac_training_non_overlap`: Directory with intermediate images to train the networks. These images are generated by script [`klf14_b6ntac_training_non_overlap`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0077_generate_non_overlap_training_images.py) * `klf14_b6ntac_training_augmented`: Directory with intermediate images used to train the networks (using augmentation to reduce overfitting). These images are generated by script [`klf14_b6ntac_exp_0078_generate_augmented_training_images.py`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0078_generate_augmented_training_images.py) * `klf14_b6ntac_seg`: Deprecated. Directory to store whole slide coarse segmentations in old experiments (e.g. `klf14_b6ntac_exp_0076_generate_training_images.py`). Of little interest for most users * `klf14_b6ntac_results`: Deprecated. Directory to store miscellanea output from some experiments. Of little interest for most users ## Casero et al. (2021). "Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations" (doi: 10.5281/zenodo.5149005) ### `aida_data_Klf14_v8_images.7z` (16.9 GB) Histology images converted to DeepZoom so that they can be visualised with [AIDA](https://github.com/alanaberdeen/AIDA). To use this, decompress this file and put the resulting `images` directory in your `AIDA/dist/data/` directory. ### `aida_data_Klf14_v8_annotations.7z` (18 GB) White adipocyte segmentations in AIDA annotation `.json` files (one contour per cell, one file per whole slide). Each slide has the following files: * `SLIDENAME.json`: Soft link to the annotations file that we want to associate to slide `SLIDENAME.ndpi`, e.g. `SLIDENAME` = `KLF14-B6NTAC-PAT-39.2d 454-16 B1 - 2016-03-17 12.16.06` * `SLIDENAME.lock`: Empty file used to tell the pipeline that `SLIDENAME.ndpi` has already been processed or is being currently processed * `SLIDENAME_coarse_mask.npz`: File with the coarse tissue segmentation of `SLIDENAME.ndpi` and the internal state of the pipeline (execution times, steps, etc) * `SLIDENAME_exp_0106_auto.json`: Annotations (all segmentations without filtering from the Auto algorithm, i.e. segmentation without object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_auto_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Auto algorithm. All contours aggregated into a single tile * `SLIDENAME_exp_0106_corrected.json`: Annotations (all segmentations without filtering from the Corrected algorithm, i.e. segmentation with object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_corrected_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Corrected algorithm. All contours aggregated into a single tile To use this, decompress this file and put the resulting `annotations` directory in your `AIDA/dist/data/` directory.</pre>

opencc-by-4.0Jul 2021View details →

zenodo48/100

Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the English Channel (ICES area 27.7.d) stock

Contents: This dataset can be completed with the : Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock, which can also be found on the Zenodo repository. This dataset contains the macroscopic and histological images of the ovaries of 214 Striped red mullet (female, Mullus surmuletus, Linnaeus 1758) collected from the English Channel stock (ICES area 27.7.d) in February 2021 (n=20), March 2021 (n=13), April 2021 (n=12), May 2021 (n=15), August 2021 (n=15), September 2021 (n=15), October 2021 (n=41), November 2021 (n=10), December 2021 (n=14), January 2022 (n=30), February 2022 (n=15) and August 2022 (n=14).   Images: <ul> <li>Macroscopic_pictures.zip: archive in zip format of 621 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 211 female Striped red mullets dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder et al., 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish’s ID number.</li> </ul> </li> </ul> <ul> <li>Histology_slides.zip : archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 484 histological slides acquired during this study.</li> <li>Data was split for smaller size downloads : <ul> <li>Histology_slides_1of5 : histological sections for individuals numbered 001 to 045</li> <li>Histology_slides_2of5 : histological sections for individuals numbered 046 to 138</li> <li>Histology_slides_3of5 : histological sections for individuals numbered 154 to 180</li> <li>Histology_slides_4of5 : histological sections for individuals numbered 196 to 270</li> <li>Histology_slides_5of5 : histological sections for individuals numbered 271 to 334</li> </ul> </li> </ul>   Data: <ul> <li>Readings.zip : archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li>Calibration : Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used.</li> <li>Homogeneity : Reading results for 96 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 96 slides belong to 16 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li>Total : Reading results for 214 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li>Macro_MULL_read_me.txt : a text file (.txt) listing the acronyms used in the Macro_MULL.xlsx file, as well as their meaning.</li> <li>Macro_MULL.xlsx : Excel file (.xlsx) containing measurements of macroscopic parameters for all 214 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (Macroscopic_pictures_Data)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode et al., 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish’s otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish’s gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> </ul> <ul> <li>Stereo_MULL_read_me.txt : a text file (.txt) listing the acronyms used in the Stereo_MULL.csv file, as well as their meaning.</li> <li>Stereo_MULL.csv : a text data file (.csv) of the stereology count results of 294 slides read during this study. Among these slides, 96 were read to test the homogeneity distribution of different cell types found throughout each ovary (16 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see Calibration folder for reading results of the 4 agents). Finally, 214 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin et al. 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with Fish_id</li> </ul> </li> </ul>

opencc-by-4.0Jun 2023View details →

zenodo48/100

Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock

Contents: This dataset can be completed with the : Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the English Channel (ICES area 27.7.d) stock, which can also be found on the Zenodo repository. This dataset contains the macroscopic and histological images of the ovaries of 103 Striped red mullet (female, Mullus surmuletus, Linnaeus 1758) collected from the Bay of Biscay stock (ICES areas 27.7.j,g & 27.8.a-c) in November 2020 (n=9), May 2021 (n=11), June 2021(n=7), July 2021 (n=15), September (n=15), October 2021 (n=3), November 2021 (n=27) and February 2022 (n=15).   Images: <ul> <li>Macroscopic_pictures.zip: archive in zip format of 290 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 103 female Striped red mullets dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder et al., 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish’s ID number.</li> </ul> </li> </ul> <ul> <li>Histology_slides.zip: archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 264 histological slides acquired during this study. Data was split for smaller size downloads : <ul> <li>Histology_slides_1of3 : histological sections for individuals numbered 062 to 094</li> <li>Histology_slides_2of3 : histological sections for individuals numbered 100 to 250</li> <li>Histology_slides_3of3 : histological sections for individuals numbered 290 to 304</li> </ul> </li> </ul>   Data: <ul> <li>Readings.zip: archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li>Calibration : Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used.</li> <li>Homogeneity : Reading results for 84 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 84 slides belong to 14 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li>Total : Reading results for 103 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li>Macro_MULL_read_me.txt : a text file (.txt) listing the acronyms used in the Macro_MULL.xlsx file, as well as their meaning.</li> <li>Macro_MULL.xlsx : Excel file (.xlsx) containing measurements of macroscopic parameters for all 103 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (Macroscopic_pictures_Data)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode et al., 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish’s otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish’s gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> </ul> <ul> <li>Stereo_MULL_read_me.txt : a text file (.txt) listing the acronyms used in the Stereo_MULL.csv file, as well as their meaning.</li> <li>Stereo_MULL.csv : a text data file (.csv) of the stereology count results of 173 slides read during this study. Among these slides, 84 were read to test the homogeneity distribution of different cell types found throughout each ovary (14 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see Calibration folder for reading results of the 4 agents). Finally, 103 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin et al. 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with Fish_id</li> </ul> </li> </ul>

opencc-by-4.0Jun 2023View details →

zenodo44/100

Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set

53 marker MELC Run in human tonsil. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.

opencc-by-4.0Apr 2020View details →

zenodo44/100

Supplementary data for "Armored with skin and bone: A combined histological and µCT study of the exceptional integument of the Antsingy leaf chameleon Brookesia perarmata (Angel, 1933)"

This project contains the supplementary µCT-scans of the whole body and a lateral flank integumentary armor of Brookesia perarmata (Angel, 1933) (Squamata: Iguania: Chamaeleonidae) belonging to the following publication: Schucht P, Rühr PT, Geier B, Glaw F & M LAmbertz (2020): Armored with skin and bone: A combined histological and µCT study of the exceptional integument of the Antsingy leaf chameleon Brookesia perarmata (Angel, 1933). Journal of Morphology. doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/jmor.21135">10.1002/jmor.21135</a>.   Whole body scan: <ul> <li>specimen: ZSM 17/2006, Zoologische Staatssammlung München</li> <li>machine: phoenix nanotom m (GE Measurement & Control)</li> <li>scan settings: <ul> <li>tube voltage = 110 kV</li> <li>ube current = 70 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.24°</li> <li>exposure time = 750 ms</li> <li>binning = 1</li> <li>averaging = 4</li> <li>voxel size = 37.8 μm</li> </ul> </li> <li>filename: Schucht_B_perarmata_whole.tif</li> </ul>   Lateral flank integumentary armor scan: <ul> <li>specimen: ZSM 862/2000, Zoologische Staatssammlung München</li> <li>machine: Skyscan 1272 device (Bruker microCT)</li> <li>scan settings: <ul> <li>tube voltage = 70 kV</li> <li>ube current = 142 μA</li> <li>target = tungsten</li> <li>filter = Al 0.5 mm</li> <li>total sample rotation = 180°</li> <li>angular step size = 0.19°</li> <li>exposure time = 1925 ms</li> <li>binning = 2x2</li> <li>averaging = 8</li> <li>random movement = 15</li> <li>voxel size = 4.4 μm</li> </ul> </li> <li>filename: Schucht_B_perarmata_osteoderm.tif</li> </ul>

opencc-by-4.0May 2020View details →

zenodo44/100

Second Ocular adnexal malignancies - Histology for Histology Correlation

This table presents the histology of the first primary and histology of the orbital malignancy. Please note that the population in this table is different from the population in the paper. The first primary in this table includes all sites including the eye and orbit.

opencc-by-4.0Dec 2019View details →

zenodo44/100

Kidney glomeruli-ROIs extracted from histological slides stained with HE or PAS.

Kidney glomeruli-ROIs extracted from histological slides stained with HE or PAS. The multicenter test set of 78 regions of interest together with annotations. ROIs were extracted from 20 WSIs representing various human kidney pathologies. WSI's came from four sources: three independent medical centers and TCGA.  Slides from three sources were stained with HE and slides from one center were stained with PAS. Slides were digitalized on Pannoramic 250 Flash II (3DHISTECH, Budapest, Hungary), Hamamatsu NanoZoomer S60 Digital slide scanner, or using Aperio AT Turbo (Leica Biosystems, Vista, CA). ROIs were extracted for x10, that corresponding to the pixel size ~10um.

opencc-by-4.0Nov 2020View details →

zenodo44/100

A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications - Supplementary Data

Videos<ul><li>Video 1 A video going through the Z stack in single slices. This is a cross- sectional view of the XRH image stack along the XY plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li>Video 2 A video going through the Y stack in single slices. This is a cross- sectional view of the XRH image stack along the XZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li>Video 3 A video going through the X stack in single slices. This is a cross- sectional view of the XRH image stack along the YZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li>Video 4 3D X-ray histology (XRH) is a µCT -based workflow tailored to fit seamlessly into current histology workflows in biomedical and pre-clinical research, as well as clinical histopathology. Microanatomical detail can be captured from standard (non-stained) formalin-fixed and paraffin-embedded (FFPE) tissue blocks.</li><li>Video 5 Average Intensity Projection (AIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Average Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li>Video 6 Maximum Intensity Projection (MIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Maximum Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li>Video 7 Standard deviation projection of the sample going through the histologically relevant plane. This is a 2D visualisation rendering the Standard Deviation of 20x single XY slices along the z- axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li></ul>* Videos 5 -7 are also referred to as "thick-slice rolls" - Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack.  The questionnaire used to collect feedback about the needs of the XRH community.<ul><li>Survey.docx</li><li>Survey.pdf</li></ul> Exemplar report of a semi-automatically generated augmented PDF file that contain sample information, imaging settings, still images with descriptive figure legends, and links to corresponding online videos<ul><li>DEMO02019-FFPE_report_99EbPXG.pdf</li></ul> = = = = = = = = = = = = = = = =  System performance data ZIP = = = = = = = = = = = = = = = =  This ZIP file contains imaging data collected through different systems and setups at the XRH facility at the μ-VIS X-ray Imaging Centre at the University of Southampton for the purpose of acceptance and/or system performance characterisation. Below is an overview of the folder structure and its contentsThe following files are X-ray imaging data collected on September 28, 2017, using the Med-X system and a Jima phantom at 55 kV peak and 7 Watts. <ul><li>20170928_MEDX_1642_JIMA_55kVp7W-2.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif.profile.xml</li></ul>This PDF document is related to a QRM MicroCT bar pattern phantom, and its specifications<ul><li>QRM-MicroCT-Barpattern-Phantom.pdf</li></ul>Graphs showing the calculated focal-spot size as a function of the X-ray power (W) for the Molybdenum rotating target calculated using Edge Modulation function testing. The performance is then compared with the performance of the Reflection target across the same range of powers. Raw data can be found in XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize folder. Test performed in July 2021.  <ul><li>XRH_202107_MoRot-testing_EdgeModFunction-QRMrecons+RotReflCompar.png</li></ul> / XRH-XT-H-225-ST_FocalSpots This directory contains radiographic data collected using the XRH system with a JIMA phantom and MoRt (Molybdenum rotating), TT (Transmission), and Reflection targets.<ul><li>20200113_XRH_Jima test MoRT 55kV 15W.tif, 20200113_XRH_Jima test MoRT 55kV 30W.tif, etc.:  These files represent radiographs taken on January 13, 2020, using the XRH system, Jima phantom, MoRT target at 55 kVp and varying wattages.</li><li>20200207_XRH_JIMA 80kV TT1a.tif, 20200207_XRH_JIMA 80kV TT1b.tif, etc. Similar to the above, these files are from February 7, 2020, and use 80 kVp with a TT target.</li><li>20231115_XRH_reflW_80kVp6W.tif, 20231115_XRH_reflW_80kVp6W_02.tif, etc. These files are from November 15, 2023, and collected using the XRH system with a Reflection target at 80 kVp and 6 Watts.</li></ul>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleRadioFromCTs_5umPixelSize This directory contains single radiographs taken with a pixel size of 5 micrometers using the Molybdenum rotating (MoRt), and the Reflection target using tungsten (W) and Molybdenum (Mo) metals./ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize This directory contains sinlge reconstruction slices of the setups mentioned above. Slices are exported from CT volumes and were used for the Edge Modulation function study.  For interpretation of the filenames in the folders listed above please see below and refer to specific files and folders for detailed information and results related to each imaging session:<ul><li><xx>kVp or <xx>kV   :Imaging at a peak voltage of <xx> kVp.</li><li><y>W   :Imaging at <y> Watts;  "." is represented with "-"; i.e. 20210705_XRH_2766_PJB_TEST03552-EQPMT_W_6-9W is acquired using a power of 6.9 W</li><li>MoRt, TT, Refl   :Molybdenum, Transmission, and Reflection targets, respectively.</li><li>_W_ and _Mo_   :Tungsten and Molybdenum target materials.</li><li>_horiz   :Reconstruction slices in line with the X-ray beam's propagation direction.</li><li>_vert   :Reconstruction slices normal to the X-ray beam's propagation direction and parallel to the detector plane.</li></ul>

opencc-by-4.0Jun 2023View details →

zenodo44/100

2 million histological images of breast cancer tumors with her2 labels

Data Description This is a 2 million set of non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human breast cancer tumor tissue. The anonymized dataset comes from a cohort of BC patients from the A. C. Camargo Cancer Center (ACCCC, N = 504). All patients were treated for breast cancer at the ACCCC between 2019 and 2021. As part of their diagnosis, in HER2 IHC score 2+ cases, patients' HER2 status was determined following the ASCO guidelines updated in 2018, with visual evaluation of IHC assay and either a FISH or DDISH test. All cases with metastasis or neoadjuvant treatment were excluded. A total of 426 H&E stained high resolution images (40x magnification) were scanned from biopsy and resection tissue samples with a Leica Aperio AT2 scanner. Ethical approval of the ACCCC study was given by the ethics committee of the Fundação Antônio Prudente. We divided the cases into the following 3 groups according to the results of the IHC and ISH tests: HER2-negative, HER2-low and HER2-high. The slides were divided into 256 px x 256 px tiles at 0.5 um/pixel magnification. Then, we used a custom trained ConvNext-tiny neural network to only include tiles from the tumor region and its environment, generating a total of 2051877 image patches. A sample is considered her2-negative with an IHC score of 0; her2-low with an IHC score of 1+ or an IHC score of 2+ with a negative ISH-based test result, and her2-high with an IHC score of 2+ with a positive ISH-based test or an IHC score of 3+. The accompanying code used for training the models is available at https://github.com/tojallab/wsi-mil

opencc-by-4.0Oct 2023View details →

zenodo44/100

Reward perseveration is shaped by GABAA-mediated dopamine pauses: Histology from Behavior Data Mice

Histology images for the experimental +HTP behavior mice in the dataset 10.5281/zenodo.10903566. This histology was used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses": <ul> <li>Ext. Fig 3, panel d</li> </ul> Specifics of the data: <ul> <li>HTP_Histo_Cohort_VX.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VX</li> <li>HTP_Histo_Cohort_VZ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VZ</li> <li>HTP_Histo_Cohort_VAJ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAJ</li> <li>HTP_Histo_Cohort_VAK.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAK</li> <li> Behavior_summary_data_HTP_Histology.csv contains the summarized fluorescence quantification per animal</li> </ul>

opencc-by-4.0Apr 2024View details →

zenodo44/100

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

<div> <h2>Creators</h2> <ul> <li><a href="https://orcid.org/0009-0000-9093-9385">Meystre Julie</a></li> <li><a href="https://orcid.org/0000-0002-7100-3749">Olivier Burri</a></li> </ul> <h2>Contributors</h2> <ul> <li><a href="https://orcid.org/0009-0002-0029-7951">Jean Jacquemier</a></li> </ul> </div> <h2>Description</h2> This dataset contains 7 <a href="https://qupath.github.io/">QuPath</a> projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains images of 14 hemispheres from height animals. <ul> <li>Nissl_1 : <ul> <li>animal 1413827 Right Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_2 : <ul> <li>animal 1413829 Right Hemisphere</li> <li>animal 1413828 Right Hemisphere</li> <li>animal 1413827 Left Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_3 : <ul> <li>animal 1413828 Left Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_4 : <ul> <li>animal 1443459 Right Hemisphere</li> <li>animal 1443460 Right Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_5 : <ul> <li>animal 1443459 Left Hemisphere</li> <li>animal 1443460 Left Hemisphere</li> </ul> </li> </ul> <ul> <li>Nissl_6 : <ul> <li>animal 1449920 Left Hemisphere</li> <li>animal 1449921 Left Hemisphere</li> <li>animal 1449921 Right Hemisphere</li> <li>animal 1449922 Left Hemisphere</li> <li>animal 1449922 Right Hemisphere</li> <li> </li> </ul> </li> <li>QuPath_LayerBoundaries_GroundTruth_20220927: <ul> <li>This is the QuPath project that contains S1HL layers annotations done by the experts and which have been used to trained the Random forest Machine Learning  method for the S1HL brain classification. It contains some images from all the eight animals.</li> </ul> </li> </ul>   <div> <h3>Animals</h3> All animal procedures were approved by the Veterinary Authorities and the Cantonal Commission for Animal Experimentation of the Canton of Vaud, according to the Swiss animal protection laws, under license number VD3516. Outbred Wistar Han rats (Janvier Laboratories, France) were ordered with their litter aged eight postnatal days (P8). Dams were housed individually and allowed to raise their own litters until experimentation on male offspring aged fourteen days (P14; N=8 animals; N=3 litters). Animals were housed in standard plastic laboratory cages, with bedding, nesting material and paper tube and ad libitum access to food (SAFE 150 SP-25) and water, cleaned once per week, and kept on a twelve-hour light-dark schedule with lights turned on at 06:30 AM, in rooms under controlled humidity and temperature. The sample size here is greater than those reported in other open source atlases <a href="https://www.zotero.org/google-docs/?1dkN18">(“Allen Reference Atlas - Mouse,” n.d.; “The Rat Brain in Stereotaxic Coordinates - 7th Edition,” n.d.)</a>. <h3>Sample preparation</h3> On postnatal day fourteen, rats were transferred to the experimental room in the morning to acclimate. The described procedure was conducted within a consistent 3-hour window of the day (09:00-12:00). Initially, the rats were deeply anesthetized using pentobarbital (intraperitoneal dose of 150 mg/kg; concentration of 150 mg/ml). This was succeeded by transcardial perfusion with ice cold 0.1 M phosphate buffer (PB; pH 7.4), followed by cold 4% paraformaldehyde (PFA) in 0.1 M  PB. Subsequently, the brain was carefully removed from the skull, postfixed at 4°C in 4% PFA overnight, and then rinsed in 0.1 M PB. The brains underwent a sequential storage process: first in a 15% sucrose solution (in 0.1 M PB) at 4°C for approximately 24 hours, followed by a 30% sucrose solution at 4°C for an additional 24 hours. The hemispheres were carefully divided along the midline, after which both right and left hemispheres were precisely sliced sagittally using a cryostat (Leica, VT-1200S) at 50 µm employing an approximate angle rotation of 4 ± 1 degrees along the anterior-posterior axis to optimize alignment with apical dendrites. These brain slices were stored in a cryoprotectant solution (30% v/v ethylene glycol; 30% m/v sucrose in 0.1 M PB) at -20°C, preserving them until immunohistochemistry assays were executed (within a maximum of two weeks from extraction to immunohistochemistry). In order to determine the cell densities in P14 rat, brain slices were immunostained using cresyl violet, a stain specifically targeting cell bodies, including the endoplasmic reticulum, also known as Nissl substance or Nissl bodies. Free-floating sections of 50 µm thickness were transferred from cryoprotectant into 0.1 M PB to thaw and eliminate any cryoprotectant remnants. Subsequently, they were transferred into 0.01 M PB to minimize salt residues before being meticulously mounted onto SuperFrost© glass slides (Thermo Fisher Scientific Inc., Gerhard Menzel B.V. & Co. KG, GE). This mounting was carried out while considering the brain’s orientation relative to the midline, from its external to internal regions. Slide-mounted sections were processed using an automated slide stainer Tissue-Tek® Prisma Plus (Sakura Finetek-Europe, NL). These sections were incubated for 6 minutes at room temperature (RT = 20°C) in a 0.5% cresyl violet solution in water (with pH adjusted to 2.85 using acetic acid), followed by a brief wash in tap water. The sections underwent dehydration through a series of ethanol concentrations (70%, 70%, 96%, 100%, 100%) with each step lasting one minute at RT. Subsequently cleared with two steps of xylene for one minute each at RT, and the sections were mounted using Pertex (Sakura Finetek-Europe, NL) before being cover-slipped using the automated glass coverslipper Tissue-Tek® Glas™ g2 (Sakura Finetek-Europe, NL). A meticulous assessment of the coloration was conducted and if the staining appeared faint, a repeat staining procedure was carried out. Immunostained slides were scanned using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel. Each brain slice was entirely scanned. Subsequently, the digital images obtained were meticulously organized and subjected to analysis using the open-source software QuPath v0.3.2 <a href="https://www.zotero.org/google-docs/?jnVnIg">(Bankhead et al., 2017)</a>.   <h2>Intructions</h2> The projects contained in this dataset have been created with QuPath v0.3.2 but could be opened with new QuPath version. <ol> <li>Download the dataset</li> <li>untar the tar balls included in this dataset</li> <li>install <a href="https://qupath.github.io">QuPath</a></li> <li>Open QuPath</li> <li>Open a project within QuPath (Files->Project...->Open Project...)</li> </ol> </div>

opencc-by-4.0Jun 2024View details →

zenodo44/100

Macroscopic, histological and stereological image dataset of Megrim (Lepidorhombus whiffiagonis) ovaries from the ICES Celtic Seas, south of Greater North sea or Bay of Biscay Ecoregions

Contents:  This dataset contains the macroscopic and histological images of the ovaries of 202 Megrim (female, Lepidorhombus whiffiagonis, Walbaum, 1792) collected from the ICES Celtic Seas, south Greater North sea or Bay of Biscay Ecoregions (Eco) in November 2019 (n=25; Eco=7h & 7j), November 2020 (n=14, Eco=7h & 7j), December 2020 (n=1, Eco=7h), May 2021 (n=15, Eco=7h & 7g), June 2021 (n=15, Eco=7h), July 2021 (n=15, Eco=7h & 7e), October 2021 (n=15, Eco=7g & 7f), October 2021 (n=6, Eco=8a & 8b & 8c), November 2021 (n=6, Eco=8a & 8b), November 2021 (n=15, Eco=7j), December 2021 (n=15, Eco=7e & 7g), January 2022 (n=15, Eco=7f), February 2022 (n=15, Eco=7g), March 2022 (n=15, Eco=7g) and May 2022 (n=15, Eco=7g).   Images: <ul> <li>Macroscopic_pictures.zip: archive in zip format of 549 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 202 female megrim dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder et al., 2022) with :  <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish's ID number.</li> </ul> </li> <li>Histology_slides.zip : archive in zip format containing the ovarian histological slides digitized using an Aperio CS (Scan Scope Console software, v.10.2.0.2352), x20 lens. The whole slide images (.svs) are of the 461 histological slides acquired during this study. </li> </ul>   Data: <ul> <li>Readings.zip : archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available.  <ul> <li>Calibration : Reading results of 3 different agents, with the first and last readings, as well as the QuPath scripts used.</li> <li>Homogeneity : Reading results for 102 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 102 slides belong to 17 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li>Total : Reading results for 202 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> <li>Macro_WHI_read_me.txt : a text file (.txt) listing the acronyms used in the Macro_WHI.xlsx file, as well as their meaning.</li> <li>Macro_WHI.xlsx : Excel file (.xlsx) containing measurements of macroscopic parameters for all 202 fish sampled during this study. The information contained in this table is as follows:  <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (Macroscopic_pictures_Data)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode et al., 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish's otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish's gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> <li>Stereo_WHI_read_me.txt : a text file (.txt) listing the acronyms used in the Stereo_WHI.csv file, as well as their meaning.</li> <li>Stereo_WHI.csv : a text data file (.csv) of the stereology count results of 287 slides read during this study. Among these slides, 102 were read to test the homogeneity distribution of different cell types found throughout each ovary (17 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see Calibration folder for reading results of the 3 agents). Finally, 202 median histological ovarian slides were read. The information contained in this table is as follows:  <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin et al. 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with Fish_id</li> </ul> </li> </ul>   Contact : This dataset was established under the MATO (MATurité Objectif des poissons par l'histologie quantitative) project, during the PhD of Carine Sauger (October 2021-2023), financed by France Fillière Pêche (FFP/2020/AM/MF/109), under the supervision of IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) and BOREA (Biologie des Organismes et Ecosystèmes Aquatiques), and with the collaboration of a research facility from the University of Caen-Normandie : CMABIO3 (Centre de Microscopie Appliquée à la Biologie). For any enquiries, please contact: carine.sauger@gmail.com or laurent.dubroca@ifremer.fr

opencc-by-4.0Oct 2023View details →

zenodo44/100

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1

Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

opencc-by-4.0Dec 2022View details →

zenodo44/100

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV2

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

opencc-by-4.0Jan 2023View details →

zenodo44/100

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV1

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

opencc-by-4.0Jan 2023View details →

zenodo44/100

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 3 FOV2

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 3 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

opencc-by-4.0Jan 2023View details →

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Macroscopic, histological and stereological image dataset of Megrim (Lepidorhombus whiffiagonis) ovaries from the ICES Celtic Seas, south of Greater North sea or Bay of Biscay Ecoregions

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