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328 results for “segmentation dataset”
Dataset for "Segmentation of Lipid Droplets in Histological Images"
<p>Datasets for the publication "Segmentation of Lipid Droplets in Histological Images" (Medical Imaging with Deep Learning (MIDL 2023), short paper track. 2023. https://openreview.net/forum?id=nTnAm_El0RC)</p>
A Trimodal Dataset: RGB, Thermal, and Depth for Human Segmentation and Action Recognition
<p>Computer vision research and popular datasets are predominantly based on the RGB modality. However, traditional RGB datasets have limitations in lighting conditions and raise privacy concerns. Integrating or substituting with thermal and depth data offers a more robust and privacy-preserving alternative. We present a public trimodal dataset comprising registered sequences of RGB, depth, and thermal data. The dataset encompasses 10 unique environments, 18 camera angles, 101 shots, and 15,618 frames which include human masks for semantic segmentation and dense labels for action classification and scene understanding. We discuss the system setup, including sensor configuration and calibration, as well as the process of generating ground truth annotations. On top, we conduct a quality analysis of our proposed dataset and provide benchmark models as reference points for human segmentation and action recognition. By employing only modalities of thermal and depth, these models yield improvements in both human segmentation and action classification.</p>
A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup
<p>We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4].</p> <table> <caption>Details of this dataset</caption> <tbody> <tr> <td>Domain</td> <td>Cases in Each Domain<br> (Training/Test)</td> </tr> <tr> <td>REFUGE</td> <td>320/80</td> </tr> <tr> <td>Drishti-GS</td> <td>50/51</td> </tr> <tr> <td>ORIGA</td> <td>500/150</td> </tr> <tr> <td>BinRushed (RIGA)</td> <td>156/39</td> </tr> <tr> <td>Magrabia (RIGA)</td> <td>76/19</td> </tr> </tbody> </table> <p>[1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570.</p> <p>[2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56.</p> <p>[3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068.</p> <p>[4] 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. SPIE, 2018, 10579: 55-62.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre> <p> </p>
The surgical instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>In the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention, we collected and annotated the surgical instrument dataset.</p>
Dataset for "Geospatial segmentation of high-resolution photovoltaic production maps for Switzerland", Frontiers Energy
<p>## Notes<br> 1. The following tif files contain the Plain-Of-Array irradiation on south-facing solar panels with different tilts {20,30,40,50,60,70} and during different seasons {summer,winter}.<br> 2. The file pattern is poa\_{season}\_tilt\_{tilt}.tif<br> 3. Read Section 2.2 of the research article for details on how these files were created. <br> 4. These values are POA and need to be converted to energy production via an efficiency factor. This factor was chosen to be a fixed value of 20\% as noted in Section 2.2 of the research article.</p>
Our processed EndoVis2017 dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed EndoVis2017 dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Our processed Kvasir-Instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed Kvasir-Instrument dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Distinction and segmentation of the two coronary arteries in the high-resolution normal multiscan CECT dataset
<p>The 2D orthoslice shown on the left is the cutting plan used in the 3D rendering (left). RCA = right coronary artery (blue), LMCA = left marginal coronary artery (pink).</p>
A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
Open the record for dataset details and reuse information.
Nepal landslide dataset for semantic segmentation
<p>This database contains images used for the semantic segmentation of landslide scars from a fully convolutional neural network U-Net.</p> <p>1. <strong>Training dataset: </strong>it contains 230 GeoTIFF 8 bits images and associated PNG masks (scars indicated in white and background in black color).</p> <p>2. <strong>Validation dataset</strong>: it contains 35 GeoTIFF 8 bits images and associated PNG masks used for U-Net validation step.</p> <p>3. <strong>Test dataset: </strong>it contains 10 GeoTIFF 8 bits images and associated PNG masks for testing.</p> <p>Also, the "SHAPEFILES_LANDSLIDES.rar" file contains the vector layers of the masked images in .shp format.</p>
Training datasets and final models from paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p> See paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation' for an explanation of how the models and datasets were created.</p> <p>The images are extracted from the following larger datasets using the RootPainter software:</p> <p>Nodules: http://doi.org/10.5281/zenodo.3753603</p> <p>Biopores: http://doi.org/10.5281/zenodo.3753969</p> <p>Roots: http://doi.org/10.5281/zenodo.3527713</p> <p> </p>
Japan landslide dataset for semantic segmentation
<p>This database contains images used for the semantic segmentation of landslide scars from a fully convolutional neural network U-Net.</p> <p>1. <strong>Training dataset: </strong>it contains 125 GeoTIFF 8 bits images and associated PNG masks (scars indicated in white and background in black color).</p> <p>2. <strong>Validation dataset</strong>: it contains 10 GeoTIFF 8 bits images and associated PNG masks used for U-Net validation step.</p> <p>3. <strong>Test dataset: </strong>it contains 10 GeoTIFF 8 bits images and associated PNG masks for testing.</p> <p>Also, the "SHAPEFILES_LANDSLIDES.rar" file contains the vector layers of the masked images in .shp format.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Tuning/Validation Set)
<p>RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.</p> <p>This is the Tuning Set (a.k.a. Validation Set in machine learning terminology) of RibFrac dataset, including 80 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-val-images.zip: 80 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-val-labels.zip: 80 annotations in NII format (nii.gz).</li> <li>ribfrac-val-info.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em></p> <p> </p> <p>The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 2)
<p>RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.</p> <p>Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 2 of RibFrac dataset, including 120 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-train-images-2.zip: 120 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-train-labels-2.zip: 120 annotations in NII format (nii.gz).</li> <li>ribfrac-train-info-2.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em></p> <p> </p> <p>The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 1)
<p>RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.</p> <p>Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 1 of RibFrac dataset, including 300 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-train-images-1.zip: 300 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-train-labels-1.zip: 300 annotations in NII format (nii.gz).</li> <li>ribfrac-train-info-1.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em></p> <p> </p> <p>The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Auto-segmentation TFRecord - eye, lens, patient datasets
<p>Auto-segmentation TFRecord - eye, lens, patient datasets</p>
Amazon Rainforest dataset for semantic segmentation V2
<p>This database contains images used for training a fully convolutional neural network for the semantic segmentation of forested areas in images from the Sentinel-2 Level 2A Satellite.</p> <p>The images refer to the RGB composition (bands 4, 3 and 2). The histogram for each image was selected as follows:</p> <ul> <li>Band 4 (red): values ranging from 103 to 2724;</li> <li>Band 3 (green): values ranging from 194 to 2888;</li> <li>Band 2 (blue): values ranging from 99 to 2798.</li> </ul> <p>After that, each band was converted to a byte type (0-255).</p> <p>The images are still divided into three main sets: training, validation and testing:</p> <ol> <li><strong>Training dataset: </strong>it contains 1.123 GeoTIFF images with 512x512 pixels and associated PNG masks (clouds indicated in white and background in black color).</li> <li><strong>Validation dataset</strong>: it contains 100 GeoTIFF images with 512x512 pixels and associated PNG masks used for validation step.</li> <li><strong>Test dataset: </strong>it contains 100 GeoTIFF images 512x512 pixels for testing.</li> </ol>
AeroPath: An airway segmentation benchmark dataset with challenging pathology
Open the record for dataset details and reuse information.
LIVECell A large-scale dataset for label-free live cell segmentation - organised by cell type
<p>This data was originally published in </p><p><a href="https://doi.org/10.1038/s41592-021-01249-6">Edlund, C., Jackson, T.R., Khalid, N. <i>et al.</i> LIVECell—A large-scale dataset for label-free live cell segmentation. <i>Nat Methods</i> <strong>18</strong>, 1038–1045 (2021). https://doi.org/10.1038/s41592-021-01249-6</a></p><p>The instance segmentation annotation data has been downloaded and organised in folders for each cell type to ease its use in demos and courses. Please, refer to the original publication and <a href="https://sartorius-research.github.io/LIVECell/">original data source</a> for further research studies. </p><p><strong>IF YOU USE THIS DATASET, PLEASE ACKNOWLEDGE THE ORIGINAL AUTHORS (Edlund et al., Nat Methods 2021)</strong></p>
The SoccerSum Dataset for Automated Detection, Segmentation, and Tracking of Objects on the Soccer Pitch
<p>The SoccerSum dataset was curated by capturing and annotating soccer videos from the Norwegian Eliteserien league. This collection spans three years, covering 2021, 2022, and 2023. It comprises 750 frames from 41 unique sequences, with 4 to 40 frames per sequence, carefully chosen to represent a diverse selection of scenarios encountered in professional soccer games.</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.