Find research datasets worth reusing

FIGURE 1 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 1 Segmentation pipeline overview. (a) Specimens are placed in iodine for staining for 2 weeks and then placed in small vials containing 99% ethanol to prevent them from moving during scanning. (b) The computed tomography (CT) scanner acquires successive X-ray images of the stepwise rotating specimen, and, using a user-defined reference image, automatically reconstructs them to produce orthogonal cross-section stacks that are used for the volume reconstruction of the specimen. (c) Volume rendering for future morphological studies is performed using Amira software. (d) Semiautomated segmentation of the brain volume of each scan (in orange) using the watershed method in Amira. (e) Schematic representation of the U-Net architecture used as the core of the pipeline for the development of a fully automated brain segmentation method. (f) The acquired brain images are used for training after preprocessing augmentation and manual creation of masks. (g) The network's prediction (in yellow) is postprocessed for smoothing out overpredicted areas (in red).

FIGURE 2 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 2 Exemplar images of full-body scans from different ant species. Three-dimensional (3D) reconstructed microcomputed tomography (micro-CT) image of (a) Acromyrmex versicolor and (b) Atta texana worker specimens, using volume rendering in Amira. (c) 2D micro-CT full body image of the Atta texana specimen (original 1000 × 1000 px). The brain area is the area with the most uniform pixel density within the whole body in its stained state, which makes it easy to recognize in most high-quality scans.

FIGURE 3 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 3 Example of semiautomated brain image segmentation. The brain area (in orange) of an Atta texana ant specimen was segmented using the watershed method in Amira; the 1000 × 1000 × 1000 px 3D image was manually postprocessed by smoothing and cropping oversegmented areas.

FIGURE 9 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 9 Prediction of ganglia in the thorax. As the tissue texture in the image is similar to that of the brain, the network accurately predicts other areas of nervous tissue in the organism. The pixel island detection step isolates the brain, but without this step neural tissue can be isolated.

FIGURE 8 3D in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 8 3D volume of ant brain reconstructed from 2D images (original 520 × 520 px) predicted by the algorithm. 3D reconstructed brain prediction of an Atta texana worker.

FIGURE 7 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 7 Pipeline performance demonstrated both for validation (top row) and testing (bottom row) sets. (a, d) Raw images of head of Acromyrmex versicolor and Carebara atoma ant specimens, cropped along the x-y axes. The manually segmented brain areas are indicated in blue. (b, e) Network predictions before postprocessing (in yellow). Areas in yellow dotted circles are pixel islands not connected to the brain area that were overpredicted. (c, f) Predictions after postprocessing (in red). The borders of the predicted areas show good agreement with the manual segmentation in both sets. Note that in overlapping manually and automatically segmented areas in b, c, e, and f, colors appear green or purple.

opencc-by-4.0May 2012View details →

Fig.ç17.A mblyops surugensis sp. nov., holotype, female (NSMT-Cr 21364). A, anterior part of body (dorsal); B, antenna (right, dorsal); C, antennal peduncle (right, lateral); D, secomd segment of mandibular palp (right); E, third segment of mandibular palp (right); F, maxillule (right); G, maxilla (right). in The Genus Amblyops (Crustacea: Mysida: Mysidae: Erythropinae) from East Asia and Australia, with Descriptions of Ten New Species

Fig.ç17.A mblyops surugensis sp. nov., holotype, female (NSMT-Cr 21364). A, anterior part of body (dorsal); B, antenna (right, dorsal); C, antennal peduncle (right, lateral); D, secomd segment of mandibular palp (right); E, third segment of mandibular palp (right); F, maxillule (right); G, maxilla (right).

opencc-by-4.0Dec 2010View details →

Fig. (18-26): (18) D. laetatorius, hind wing; (19) Netelia sp., hind wing; (20) Netelia sp., frontal view of head; (21) D. laetatorius, propodeum; (22) Syrphophilus bizonarius, propodeum; (23) D. laetatorius, dorsal aspect of metasoma; (24) Netelia sp., lateral aspect of first metasomal segment showing glymma; (25) Exetastes syriacus, ovipositor; (26) Exeristes roborator, ovipositor. in Ichneumonidae from the Suez Canal region Egypt (Hymenoptera, Ichneumonoidea)

Fig. (18-26): (18) D. laetatorius, hind wing; (19) Netelia sp., hind wing; (20) Netelia sp., frontal view of head; (21) D. laetatorius, propodeum; (22) Syrphophilus bizonarius, propodeum; (23) D. laetatorius, dorsal aspect of metasoma; (24) Netelia sp., lateral aspect of first metasomal segment showing glymma; (25) Exetastes syriacus, ovipositor; (26) Exeristes roborator, ovipositor.

opencc-by-4.0Oct 2018View details →

FIGURES 11–14. Euglossine taxa not exhibiting elongate, necklike prothoracic segment. 11 in On Egg Eclosion and Larval Development in Euglossine Bees

FIGURES 11–14. Euglossine taxa not exhibiting elongate, necklike prothoracic segment. 11. Eulaema (Apeulaema) nigrita modified from Zucchi et al. (1969a: fig. 8) and 12, 13. Eufriesea surinamensis from Rozen (2016: figs. 6, 7) (both of which were identified as predefecating). 14. Exaerete smaragdina, identified as postdefecating in Garófalo and Rozen (2001: fig. 28), presumably was in an early stage that had not yet started to develop pupal tissue internally.

opencc-by-4.0Oct 2023View details →

An urban traffic dataset composed of visible images and their semantic segmentation generated by the CARLA simulator

If you use this dataset please cite this paper: Rosende, S.B.; Gavilán, D.S.J.; Fernández-Andrés, J.; Sánchez-Soriano, J. An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator. Data 2024, 9, 4. <a href="https://doi.org/10.3390/data9010004">https://doi.org/10.3390/data9010004</a> A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. The images have been generated using the CARLA simulator (but would be like those that could be obtained with fixed aerial cameras or by using AUVs) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.

opencc-by-4.0Jan 2022View details →

Figs 10−16. Apical abdominal segments. 10−15 in Omalium gildenkovi (Coleoptera: Staphylinidae: Omaliinae), a new species from the central part of European Russia

Figs 10−16. Apical abdominal segments. 10−15 – Omalium gildenkovi; 16 – O. exiguum. 10 – male sternite VIII; 11 – male tergite VIII; 12 – female sternite VIII; 13 – female tergite VIII; 14 – male genital segment; 15−16 – female genital segment. Scale bars 0.1 mm. Рис. 10−16. Вершинные брюшные сегменты. 10−15 – Omalium gildenkovi; 16 – O. exiguum. 10 – стернит VIII самца; 11 –тергит VIII самца; 12 – стернит VIII самки; 13 – тергит VIII самки; 14 – генитаΛьный сегмент самца; 15−16 – генитаΛьный сегмент самки. Масштабные Λинейки 0.1 мм.

opencc-by-4.0Nov 2021View details →

SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark

This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI. Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes: 1.&ensp;&ensp;&ensp;&ensp;Modic changes (type I, II or III) 2.&ensp;&ensp;&ensp;&ensp;Upper and lower endplate changes / Schmorl nodes (binary) 3.&ensp;&ensp;&ensp;&ensp;Spondylolisthesis (binary) 4.&ensp;&ensp;&ensp;&ensp;Disc herniation (binary) 5.&ensp;&ensp;&ensp;&ensp;Disc narrowing (binary) 6.&ensp;&ensp;&ensp;&ensp;Disc bulging (binary) 7.&ensp;&ensp;&ensp;&ensp;Pfirrman grade (grade 1 to 5).  All radiological gradings are provided per IVD level. <div>This dataset, and the associated public benchmark, are described in this paper: <a href="https://www.nature.com/articles/s41597-024-03090-w" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03090-w</a></div> <div>The public segmenation challenge can be found here: <a href="https://spider.grand-challenge.org/" target="_blank" rel="noopener">https://spider.grand-challenge.org/</a></div> <div> </div> <div>When using this dataset, please cite this dataset with the correct DOI, and also cite the afformentioned paper.</div>

Segmentation masks INbreast

This dataset provides manually created segmentation masks of the images in the INbreast dataset by I.C. Moreira et al[1]. The masks are saved as nrrd files with pixel-wise ground truth for background (0), breast (1), and pectoral muscle (2) (when present). This dataset is created for the development of a mammogram segmentation model[2]. Segmentation masks were created in three steps, first initialization of the breast boundary by Otsu thresholding[3], second a pectoral muscle initialization for MLO images, and lastly a manual adjustment of the mask, as show in Figure 1. For the MLO views, the already publicly-available annotations of the pectoral muscle were used as the initialization. Finally, each segmentation mask was checked visually and adjusted manually using ITK-SNAP 3.6.0[4] by one of four medical imaging scientists with experience in mammography. This also includes adding pectoral muscle annotation were it was visible in CC views. [1] I. C. Moreira et al., "INbreast: Toward a Full-field Digital Mammographic Database", Acad. Radiol. 19(2), 236–248 (2012) [2] S.D. Verboom et al., "Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors", Journal of Medical Imaging, 11(1), 014001 (2023) [3] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms", IEEE Trans. Syst. Man. Cybern. 9(1), 62–66 (1979) [4] P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability", Neuroimage 31(3), 1116–1128 (2006)

Segmentation masks mini-MIAS

This dataset provides manually created segmentation masks of the images in the mini-MIAS dataset by J Suckling et al[1] (available at http://peipa.essex.ac.uk/info/mias.html). The masks are saved as nrrd files with pixel-wise ground truth for background (0), breast (1), and pectoral muscle (2) (when present). This dataset is created for the development of a mammogram segmentation model[2]. Segmentation masks were created in three steps, first initialization of the breast boundary by Otsu thresholding[3], second a pectoral muscle initialization with Otsu thresholding, and lastly a manual adjustment of the mask. The pectoral muscle initialization was done by re-applying the Otsu thresholding method after excluding the background. Finally, each segmentation mask was checked visually and adjusted manually using ITK-SNAP 3.6.013[4] by one of four medical imaging scientists with experience in mammography.  [1] J Suckling et al, "The Mammographic Image Analysis Society Digital Mammogram Database" Exerpta Medica. International Congress Series 1069, 375-378 (1994) [2] S.D. Verboom et al., "Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors", Journal of Medical Imaging, 11(1), 014001 (2023) [2] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Trans. Syst. Man. Cybern. 9(1), 62–66 (1979) [3] P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability," Neuroimage 31(3), 1116–1128 (2006)

Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation

A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches. The dataset includes a trained DeepLabV3-Resnet18 network for detection of large spots on a black background. The network can be opened by loading "2D_LargeSpots_2cl_DeepLabV3.mibCfg" file by<ul><li>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </li><li>Drag and drop of the config file into DeepMIB window</li></ul>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a>

opencc-by-4.0Aug 2023View details →

Research data for Taltal segment tomography article

Initial and final data for the local earthquake tomography experiment performed in the Taltal segment, northern Chile. Article is submitted to G-cubed in August 2023. Content: - Initial earthquake catalog (*eqks) - Initial P- and S-wave arrival times (*data) - Initial 1D seismic velocity model (mod.1d) - Final earthquake catalog (*eqks) - Final P- and S-wave arrival times (*data) - Final seismic velocity models (Vp, Vs, Vp/Vs) in xyzv format - Station list  - REST user guide - Tomography notes

opencc-by-4.0Jan 2024View details →

RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment

This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level..  The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops. The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number> _1: Blue _2: Green _3: Red _4: Near Infrared _5: RedEdge Example: An image name  IMG_0200_3 represents the scene number 200 in Red channel This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format. The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article.  Field experimentation roles: The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy. Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'.  Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.

dryad40/100

Code and example images from: recolorize: An R package for flexible color segmentation of biological images

Color pattern variation provides biological information in fields ranging from disease ecology to speciation dynamics. Comparing color pattern geometries across images requires color segmentation, where pixels in an image are assigned to one of a set of color classes shared by all images. Manual methods for color segmentation are slow and subjective, while automated methods can struggle with high technical variation in aggregate image sets. We present recolorize, an R package toolbox for human-subjective color segmentation with functions for batch-processing low-variation image sets and additional tools for handling images from diverse (high variation) sources. The package also includes export options for a variety of formats and color analysis packages. This paper illustrates recolorize for three example datasets, including high variation, batch processing, and combining with reflectance spectra, and demonstrates the downstream use of methods that rely on this output.

opencc-zeroJan 2024View details →

Images supporting: Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation

Two image datasets (as zip files) including all images analyzed in the manuscript Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation. Images are of pancreatic adenocarcinoma (PDAC) cystic spheroid samples grown in either BME or Matrigel. Some images have background noise in the form of iron oxide nanoparticles introduced to them.