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282 results for “image segments”
Preprocessed Data used by "Machine Learning Enabled Brain Segmentation for Small Animal Image Registration"
<p>Data, preprocessed by the SAMRI package, used to train the models in "Machine Learning Enabled Brain Segmentation for Small Animal Image Registration". The models can be found <a href="https://zenodo.org/record/3759361#.XrKrgBMzZhE">here</a>.</p>
Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
<p><b>Objectives: </b>To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.</p> <p><b>Background: </b>Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.</p> <p><b>Methods: </b>Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.</p> <p><b>Results: </b>The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.</p> <p><b>Conclusions: </b>An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.</p>
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
<p><strong>Content</strong></p> <p>This repository contains pre-trained computer vision models, data labels, and images used in the pre-print publication "A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains":</p> <ol> <li><em>ADPdevkit</em>: a folder containing the 50 validation ("tuning") set and 50 evaluation ("segtest") set of images from the Atlas of Digital Pathology database formatted in the VOC2012 style--the full database of 17,668 images is available for download from the original website</li> <li><em>VOCdevkit</em>: a folder containing the relevant files for the PASCAL VOC2012 Segmentation dataset, with both the trainaug and test sets</li> <li><em>DGdevkit</em>: a folder containing the 803 test images of the DeepGlobe Land Cover challenge dataset formatted in the VOC2012 style</li> <li><em>cues</em>: a folder containing the pre-generated weak cues for ADP, VOC2012, and DeepGlobe datasets, as required for the SEC and DSRG methods</li> <li><em>models_cnn</em>: a folder containing the pre-trained CNN models</li> <li><em>models_wsss</em>: a folder containing the pre-trained SEC, DSRG, and IRNet models, along with dense CRF settings</li> </ol> <p><strong>More information</strong></p> <p>For more information, please refer to the following article. <strong>Please cite this article when using the data set.</strong></p> <p>@misc{chan2019comprehensive,<br> title={A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains},<br> author={Lyndon Chan and Mahdi S. Hosseini and Konstantinos N. Plataniotis},<br> year={2019},<br> eprint={1912.11186},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p> <p>For the full code released on GitHub, please visit the repository at: <a href="https://github.com/lyndonchan/wsss-analysis">https://github.com/lyndonchan/wsss-analysis</a></p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br> Lyndon Chan<br> lyndon.chan@mail.utoronto.ca<br> http://orcid.org/0000-0002-1185-7961</p>
Serial two-photon tomography (STPT) of the brain through bi-channel image registration and deep learning segmentation (BIRDS)
<p>We have developed an open-source software called BIRDS (bi-channel image registration and deep learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.</p>
"Contrast based circular approximation for accurate and robust optic disc segmentation in retinal images" - Code
<p>A new method for automatic optic disc localization and segmentation is presented. The localization procedure combines vascular and brightness information to provide the best estimate of the optic disc center which is the starting point for the segmentation algorithm. A detection rate of 99.58% and 100% was achieved for the Messidor and ONHSD databases, respectively. A simple circular approximation to the optic disc boundary is proposed based on the maximum average contrast between the inner and outer ring of a circle centered on the estimated location. An average overlap coefficient of 0.890 and 0.865 was achieved for the same datasets, outperforming other state of the art methods. The results obtained confirm the advantages of using a simple circular model under non ideal conditions as opposed to more complex deformable models.</p>
An adaptive segment anything model for water level measurement with staff gauge images: staff gauge images and in-situ water levels
<p><span>We installed a commercial time-lapse camera on an urban lake in South China to collect the staff gauge images with a interval of 15 minutes. The data samples spanned from May 27th, 0:00 am, to July 6th, 11:00 am, 2023 (UTC+8), including a total of 2691 images. All the collected images were manually read to derive the stages as ground truth. The image and water level data is stored in this dataset.</span></p> <p> </p>
DepthMars Dataset for Semantic Segmentation of the Martian Surface from Rover Images
<p>This dataset is resulted from a research article, "DepthFormer: Depth-Enhanced Transformer Network for Semantic Segmentation of the Martian Surface from Rover Images", which includes surface images on Mars collected by the Zhurong rover along its traverse, depth images generated from stereo images, and corresponding manually labeled images.</p>
Coastal Satellite Image Segmentation (Water and Land) Labels: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)
<p>Contained here are jpegs containing coastal RGB satellite images along with a water vs. land mask. Each image is 256 pixels by 256 pixels. </p> <p>Geographic scope: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)</p> <p>Temporal range: 1984 to 2022</p> <p>Satellites: Landsat 5, 7, 8 and Sentinel-2</p> <p>All images were downloaded from Google Earth Engine using CoastSat download tools.</p> <p>The datasets are arranged into 'train', 'val', and 'test' folders. Within each of those folders are two folders 'a' and 'b'. 'a' contains the images (RGB), whereas 'b' contains the labels (land vs. water mask).</p> <p>All images were augmented with the four following augmentations: horizontal flip, vertical flip, 90 degree clockwise rotation, 90 degree counterclockwise rotation, and a horizontal+vertical flip. </p> <p>For training a new segmentation model, it is advised to not do any of these rotational or flip augmentations since they have already been performed. Instead, possibly experiment with other augmentations like introducing noise into the imagery.</p> <p>These images were used to train an image-to-image translation generative adversarial network. The code and model weights (generator and discriminator) are available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>To get to the files locally, you can download the .zip from Zenodo and then unzip the .zip file.</p>
Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data
<p>This is the data repository for Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data.</p> <p>Catalog:</p> <ol> <li>Intermediate data used in plotting: CANVAS_source_data.zip <ol> <li>Single cell monocyte data: monocyte.h5ad</li> <li>qPCR table: qPCR_1013.csv</li> <li>NanoString GeoMx cell composition: fig6b.csv</li> </ol> </li> <li>Pretrained CANVAS model: checkpoint-1999.pth</li> </ol> <p>The source IMC data is avaiable at: https://zenodo.org/records/7760826</p>
Example data for "Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data"
<p>This repository includes two example dataset and configurations for running CANVAS (https://github.com/tanjimin/CANVAS).</p> <p>The repostory is structured as follows:</p> <p>├── Kim_2022<br>│ ├── configs<br>│ │ ├── config.yaml<br>│ │ └── preprocess<br>│ │ ├── channels_vis_strength.yaml<br>│ │ └── selected_channels_w_color.yaml<br>│ └── data<br>│ └── raw_data<br>│ ├── common_channels.txt<br>│ └── image_files<br>└── Sorin_2023<br> ├── configs<br> │ ├── config.yaml<br> │ └── preprocess<br> │ ├── channels_vis_strength.yaml<br> │ └── selected_channels_w_color.yaml<br> └── data<br> └── raw_data<br> ├── common_channels.txt<br> └── image_files</p> <p> </p> <p>The source IMC data from this repository are from Kim et al. 2022 (https://www.nature.com/articles/s41592-022-01657-2) and Sorin et al. 2023 (https://www.nature.com/articles/s41586-022-05672-3). They are avaiable at: https://zenodo.org/records/4110560 and https://zenodo.org/records/7760826.</p>
Attention-UNet model to segment braided palaeochannels from optical images
<p>CNN model to detect braided palaeochannels from optical remotely sensed images. Includes supporting information, and location of study areas. Version 3.</p>
Materials in Vessels Dataset, Annotated images of materials in transparent vessels for semantic segmentation
<p> Data set of materials in vessels<br> The handling of materials in glassware vessels is the main task in chemistry laboratory research as well as a large number of other activities. Visual recognition of the physical phase of the<br> materials is essential for many methods ranging from a simple task such as fill-level evaluation to the<br> identification of more complex properties such as solvation, precipitation, crystallization and phase<br> separation. To help train neural nets for this task, a new data set was created. The data set contains a<br> thousand images of materials, in different phases and involved in different chemical processes, in a<br> laboratory setting. Each pixel in each image is labeled according to several layers of classification, as<br> given below:</p> <p>a. Vessel/Background: For each pixel assign value of one if it is part of the vessel and zero otherwise.<br> This annotation was used as the ROI map for the valve filter method.</p> <p>b. Filled/Empty: This is similar to the above, but also distinguishes between the filled and empty<br> regions of the vessel. For each pixel, one of the following three values is assigned:0 (background); 1<br> (empty vessel); or 2 (filled vessel).</p> <p>c. Phase type: This is similar to the above but distinguishes between liquid and solid regions of the<br> filled vessel. For each pixel, one of the following four values: 0 (background); 1 (empty vessel); 2<br> (liquid); or 3 (solid).</p> <p>d. Fine-grained physical phase type: This is similar to the above but distinguishes between specific<br> classes of physical phase. For each pixel, one of 15 values is assigned: 1 (background); 2 (empty<br> vessel); 3 (liquid); 4 (liquid phase two, in the case where more than one phase of the liquid appears in<br> the vessel); 5 (suspension); 6 (emulsion); 7 (foam); 8 (solid); 9 (gel); 10 (powder); 11 (granular); 12<br> (bulk); 13 (solid-liquid mixture); 14 (solid phase two, in the case where more than one phase of solid<br> exists in the vessel): and 15 (vapor).<br> The annotations are given as images of the size of the original image, where the pixel value is the<br> class number. The annotation of the vessel region (a) is used in the ROI input for the valve filter net .</p> <p>4.1. Validation/testing set<br> The data set is divided into training and testing sets. The testing set is itself divided into two subsets;<br> one contains images extracted from the same YouTube channels as the training set, and therefore was<br> taken under similar conditions as the training images. The second subset contains images extracted<br> from YouTube channels not included in the training set, and hence contains images taken under<br> different conditions from those used to train the net.</p> <p>4.2. Creating the data set<br> The creation of a large number of images with a variety of chemical processes and settings could have<br> been a daunting task. Luckily, several YouTube channels dedicated to chemical experiments exist<br> which offer high-quality footage of chemistry experiments. Thanks to these channels, including<br> NurdRage, NileRed, ChemPlayer, it was possible to collect a large number of high-quality images in a<br> short time. Pixel-wise annotation of these images was another challenging task, and was performed by<br> Alexandra Emanuel and Mor Bismuth.</p> <p>For more details see: <a href="https://arxiv.org/pdf/1708.08711.pdf">Setting attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels</a></p> <p>This dataset was first published in 2017.8</p> <p>For newer and Bigger datasets see</p> <p>https://zenodo.org/record/4736111#.YbG-RrtyZH4</p> <p>https://zenodo.org/record/3697452#.YbG-TLtyZH4</p> <p> </p>
PASTIS-R - Panoptic Segmentation of Radar and Optical Satellite image TIme Series
<p>Extension of the <a href="https://zenodo.org/record/5012942#.YaUaQ7so-V6">PASTIS benchmark</a> with radar and optical image time series.</p> <p>See associated <a href="https://arxiv.org/abs/2112.07558v1">article</a> for more details.</p>
◂Fig. 6 Scanning electronmicroscopy images of Ramisyllis kingghidorahi n. sp. A Anterior region up to first 17 segments, dorsal view. B Prostomium in detail, anterodorsal view (broken antennae on stub). C Prostomium and first segments in detail showing dorsal bands of cilia, dorsal view. D–F Pores on dorsal cirri. Scale bars: 1 mm A, 200 µm B, 300 µm C, 50 µm E, 30 µm D, F in Ramisyllis kingghidorahi n. sp., a new branching annelid from Japan
◂Fig. 6 Scanning electronmicroscopy images of Ramisyllis kingghidorahi n. sp. A Anterior region up to first 17 segments, dorsal view. B Prostomium in detail, anterodorsal view (broken antennae on stub). C Prostomium and first segments in detail showing dorsal bands of cilia, dorsal view. D–F Pores on dorsal cirri. Scale bars: 1 mm A, 200 µm B, 300 µm C, 50 µm E, 30 µm D, F
◂Fig. 10 Scanning electron microscopy images of Ramisyllis kingghidorahi n. sp., posterior-most regions and epithelium details. A–D Posterior ends. Arrow in C and D points to heavily ciliated anus. E– G Minute crests on the dorsal surface of midbody segments. Arrows point to crests laterally located on the dorsal surface. H Dorsal surface of posterior segments. I Clumps of cilia on dorsal surface of proventricular segments. Arrows pointing to pores in H. Scale bars: 100 µm A, B, I, 50 um C, G, 5 µm D, E,4 µm F, and 3 µm H in Ramisyllis kingghidorahi n. sp., a new branching annelid from Japan
◂Fig. 10 Scanning electron microscopy images of Ramisyllis kingghidorahi n. sp., posterior-most regions and epithelium details. A–D Posterior ends. Arrow in C and D points to heavily ciliated anus. E– G Minute crests on the dorsal surface of midbody segments. Arrows point to crests laterally located on the dorsal surface. H Dorsal surface of posterior segments. I Clumps of cilia on dorsal surface of proventricular segments. Arrows pointing to pores in H. Scale bars: 100 µm A, B, I, 50 um C, G, 5 µm D, E,4 µm F, and 3 µm H
◂Fig. 9 Scanning electron microscopy images of branches of Ramisyllis kingghidorahi n. sp. A–F Midbody branching regions with segments of different morphologies, as long as wide with long dorsal cirri in A–C, much longer with short dorsal cirri in D, E and F Details of cirri alternation in length. A, C, E–F In dorsal view; B and D in ventral view. Scale bars: 200 µm A, C, 100 µm B, F, 400 µm D, and 500 µm E in Ramisyllis kingghidorahi n. sp., a new branching annelid from Japan
◂Fig. 9 Scanning electron microscopy images of branches of Ramisyllis kingghidorahi n. sp. A–F Midbody branching regions with segments of different morphologies, as long as wide with long dorsal cirri in A–C, much longer with short dorsal cirri in D, E and F Details of cirri alternation in length. A, C, E–F In dorsal view; B and D in ventral view. Scale bars: 200 µm A, C, 100 µm B, F, 400 µm D, and 500 µm E
◂Fig. 8 Light microscope images of living specimens of Ramisyllis kingghidorahi n. sp. A Branching point. B, E–G Posterior ends showing pygidia. C, D, H, I Midbody segments in regions of long dorsal cirri. Arrows point to the ventral blood vessel in H and the digestive tract in I.A, D, E, and I in dorsal view. B, C, F and H in ventral view. G In lateral view. Scale bars: 500 µm A, E, 200 µm B, C, D, 100 µm F, H, I, and 50 µm G in Ramisyllis kingghidorahi n. sp., a new branching annelid from Japan
◂Fig. 8 Light microscope images of living specimens of Ramisyllis kingghidorahi n. sp. A Branching point. B, E–G Posterior ends showing pygidia. C, D, H, I Midbody segments in regions of long dorsal cirri. Arrows point to the ventral blood vessel in H and the digestive tract in I.A, D, E, and I in dorsal view. B, C, F and H in ventral view. G In lateral view. Scale bars: 500 µm A, E, 200 µm B, C, D, 100 µm F, H, I, and 50 µm G
RIGA+ Dataset for Unsupervised Domain Adaptation in Medical Image Segmentation
<p>Different from the previous combined multi-domain dataset for unsupervised domain adaptation (UDA) in medical image segmentation, this multi-domain fundus image dataset contains annotations made by the same group of ophthalmologists. Hence the annotator bias among different datasets can be mitigated. Therefore, this dataset can provide a relatively fair benchmark for evaluating UDA methods in fundus image segmentation.</p> <p>This dataset is based on the RIGA[1] dataset and MESSIDOR[2] dataset. We appreciate their efforts devoted by the authors of [1] and [2].</p> <p>The six duplicated cases in the RIGA dataset are filtered out according to the <a href="https://www.adcis.net/en/third-party/messidor/">Errata</a>. We also remove the duplicated cases that exist in both the RIGA dataset and the MESSIDOR dataset by hash value matching.</p> <table align="center"> <caption>Details of the RIGA+ dataset</caption> <thead> <tr> <th scope="row">Domain</th> <th scope="col">Dataset</th> <th scope="col"> <p>Labeled Samples</p> <p>(Train+Test)</p> </th> <th scope="col"> <p>Unlabeled</p> <p>Samples</p> </th> </tr> </thead> <tbody> <tr> <th scope="row">Source</th> <td>BinRushed</td> <td>195 (195+0)</td> <td>0</td> </tr> <tr> <th scope="row">Source</th> <td>Magrabia</td> <td>95 (95+0)</td> <td>0</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE1</td> <td>173 (138+35)</td> <td>227</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE2</td> <td>148 (118+30)</td> <td>238</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE3</td> <td>133 (106+27)</td> <td>252</td> </tr> </tbody> </table> <p>[1] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics, 2018, 10579: 105790B.</p> <p>[2] Decencière E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed image database: the Messidor database[J]. Image Analysis & Stereology, 2014, 33(3): 231-234.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code>@inproceedings{hu2022domain, title={Domain Specific Convolution and High Frequency Reconstruction based Unsupervised Domain Adaptation for Medical Image Segmentation}, author={Shishuai Hu and Zehui Liao and Yong Xia}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2022}, organization={Springer} }</code></pre> <p> </p>
Normalized CT images and reference segmentations of thoracic and lumbar vertebrae from the CSI 2014 workshop
<p>This is the dataset of the vertebra segmentation challenge of the <a href="http://csi-workshop.weebly.com/challenges.html">CSI 2014 workshop</a> that was held in conjunction with MICCAI 2014.</p> <ul> <li><strong>1-10</strong>: Training set, scans of 10 young adult (16-35 years old)</li> <li><strong>11-15</strong>: Test set, scans of 5 young adult (20-35 years old)</li> <li><strong>16-20</strong>: Test set, scans of 5 patients with vertebral compression fractures </li> </ul> <p>Scans were acquired at the Department of Radiological Sciences, University of California, Irvine, School of Medicine and were published under the <a href="http://opendatacommons.org/licenses/pddl/1.0/">ODC Public Domain Dedication and License</a> on <a href="http://spineweb.digitalimaginggroup.ca/">SpineWeb</a> (datasets 2 and 15). The dataset and challenge is further described in this publication: <a href="http://dx.doi.org/10.1016/j.compmedimag.2015.12.006">A multi-center milestone study of clinical vertebral CT segmentation</a></p> <p>The data that is published here has been normalized:</p> <ul> <li>Voxel values are Hounsfield values and have been clipped to [-1000, 3095]</li> <li>Image origin has been set to 0,0,0</li> <li>Image orientation has been standardized to RAI orientation</li> <li>Small islands and other obvious mistakes have been removed</li> <li>Segmentation masks have been smoothed with a 2x2x2 median filter</li> <li>Vertebrae have been anatomically labeled (8 = T1, 9 = T2, ..., 24 = L5)</li> <li>Because not always all visible vertebrae were segmented in the original data, only segmentations of the thoracic and lumbar vertebrae have been retained</li> </ul> <p><strong>License</strong></p> <p>This dataset is released under the <a href="https://opendatacommons.org/licenses/by/1-0/">Open Data Commons Attribution License</a> (which the original license allows me to do). When using this dataset for publication of any kind, please reference the following paper to meet the attribution requirement:</p> <blockquote> <p>Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P, Hammernik K, Urschler M, Ibragimov B, Korez R, Vrtovec T, Castro-Mateos I, Pozo JM, Frangi AF, Summers RM, Li S. A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph. 2016; 49:16-28. doi: 10.1016/j.compmedimag.2015.12.006.</p> </blockquote> <p>There is no need to reference this upload, referencing the original authors is sufficient.</p> <p><strong>Notes</strong></p> <p>The dataset contains 2 cases with only 4 lumbar vertebrae in which L5/S1 has not been segmented (i.e., label 25 is missing).</p> <p>The resolution and segmentation quality of the diseased cases (16-20) is quite low.</p>
SNEMI3D: 3D Segmentation of neurites in EM images
<p>In this challenge, a full stack of electron microscopy (EM) slices will be used to train <strong>machine-learning algorithms</strong> for the purpose of <strong>automatic</strong> <strong>segmentation of neurites in 3D</strong>. This imaging technique visualizes the resulting volumes in a highly anisotropic way, i.e., the x- and y-directions have a high resolution, whereas the z-direction has a low resolution, primarily dependent on the precision of serial cutting. EM produces the images as a projection of the whole section, so some of the neural membranes that are not orthogonal to a cutting plane can appear very blurred. None of these problems led to major difficulties in the manual labeling of each neurite in the image stack by an expert human neuro-anatomist.</p> <p>In order to gauge the current state-of-the-art in automated neurite segmentation on EM and compare between different methods, we are organizing a 3D Segmentation of neurites in EM images (SNEMI3D) challenge in conjunction with the<a href="http://www.biomedicalimaging.org/2013/program/isbi-challenges/"> ISBI 2013 conference</a>. For this purpose, we are making available a large training dataset of mouse cortex in which the neurites have been manually delineated. In addition, we also provide a test dataset where the 3D labels are not available. The aim of the challenge is to compare and rank the different competing methods based on their<strong> object classification accuracy</strong> in three dimensions.</p> <p>The <strong>image data</strong> used in the challenge was produced by <a href="http://lichtmanlab.fas.harvard.edu/">Lichtman Lab at Harvard University</a> (Daniel R. Berger, Richard Schalek, Narayanan "Bobby" Kasthuri, Juan-Carlos Tapia, Kenneth Hayworth, Jeff W. Lichtman) and manually annotated by <a href="http://lichtmanlab.fas.harvard.edu/people/daniel-berger">Daniel R. Berger</a>. Their corresponding biological findings were published in <a href="http://www.ncbi.nlm.nih.gov/pubmed/26232230">Cell (2015)</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.