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21 results for “Lesion Segmentation”
Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 2
<p>This archive contains part 2 of Shift Benchmark on Multiple Sclerosis lesion segmentation data. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. This part is contains data collected from several different sources and distributed under a CC BY NC SA 4.0 license. Part 1 of the data is available <a href="https://zenodo.org/record/7051658">here</a>. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. To find out more about the Shifts Project, please visit https://shifts.ai .</p>
MICCAI 2016 MS lesion segmentation challenge: supplementary results
<p>This package contains supplementary material for our article prepared for publication and under revision. It contains omitted results due to space limits of the article as well as detailed, patient per patient and team per team results for all metrics. Additional figures redundant with those of the article are also provided. </p> <p>The readme file Readme_SupplementalMaterial.txt provides details about each individual file content.</p>
Generated Metastatic CT 3D Femurs with lesions segmentation 1/3
<p>This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al. </p> <p><strong>Three repositories are needed </strong>to form the full archive. When all are downloaded, they can be deflated with the following bash command : </p> <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre> <p> </p> <p>This is the first part (1/3)</p> <p>part 2/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824177" target="_blank" rel="noopener"> 10.5281/zenodo.13824177 </a></p> <p>part 3/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824179" target="_blank" rel="noopener"> 10.5281/zenodo.13824179 </a></p> <p> </p> <p>Generated CT Scans are organized in two directories: one for files generated with DDPM this one) and one without DDPM.</p> <p>├── Generated_3DCT_Metastatic_Femurs_with_DDPM <br>│ ├── img<br>│ ├── lbl<br>│ └── msk</p> <p>└── Generated_3DCT_Metastatic_Femurs_NO_DDPM<br> ├── img<br> ├── lbl<br> └── msk</p> <p>The content of each directory is as follows: </p> <ul> <li>img : the generated 3D CT scan (nii.gz format) from the 26 healthy femurs</li> <li>lbl : the lesions segmentation (nii.gz format) of the generated 3D CT scan</li> <li>msk : the segmentations of the 26 healthy femurs (nii.gz)</li> </ul> <p>A given filename corresponds to in img and lbl directories :</p> <ul> <li>./lbl/MEK03les0MEK14.nii.gz is the lesions segmentation of ./img/MEK03les0MEK14.nii.gz.</li> <li>./lbl/MEK03les197MEK28.nii.gz is the lesions segmentation of ./img/MEK03les197MEK28.nii.gz</li> </ul> <p>But in msk, the corresponding femur mask for these examples is ./msk/MEK03.nii.gz</p> <p> </p>
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.
<p>This multi-center dataset consists of 250 expert-annotated magnetic resonance imaging stroke cases. It is the training dataset for the Ischemic Stroke Lesion Segmentation Challenge (ISLES'22).</p> <p>For each case, an expert level annotation of the stroke lesions is included along with the following three imaging sequences: Fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI, b=1000) and its corresponding apparent diffusion coefficient (ADC) map. All imaging data and annotations are released in the Neuroimaging Informatics Technology Initiative (NIfTI) format (https://nifti.nimh.nih.gov/nifti-1), according to the BIDS convention. All imaging data are released in the native space without prior registration. Prior to release, skull-stripping was performed to de-identify patients.</p> <p>Image acquisition was performed on one of the following devices: 3T Philips MRI scanners (Achieva, Ingenia), 3T Siemens MRI scanner (Verio) or 1.5T Siemens MAGNETOM MRI scanners (Avanto, Aera). All images were obtained by healthcare professionals as part of the clinical imaging routine for stroke patients at three different stroke centers and imaging data was collected retrospectively for different clinical studies. Computer-readable scanner metadata from the Digital Imaging and Communications in Medicine (DICOM) header in the JSON file format is provided with the datasets if available.</p> <p>For a full dataset description, see the <a href="https://arxiv.org/abs/2206.06694">ISLES'22 preprint</a>.</p> <p>More information about the ISLES'22 challenge can be found in <a href="https://isles22.grand-challenge.org/">grand challenge</a> and in our official <a href="http://www.isles-challenge.org/">challenge website</a>.</p> <h3>Please cite the following works when using this dataset:</h3> <ul> <li>de la Rosa, Ezequiel, et al. <strong>DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.</strong> <em>Nature Communications</em> 16.1 (2025): 7357.</li> <li>Hernandez Petzsche, Moritz R., et al. <strong>ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.</strong> <em>Scientific data</em> 9.1 (2022): 762.</li> </ul>
Open data repository, An et al., Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images
<p>Open data repository of journal article "Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images" by Jeehye An <em>et al. </em>At time of publication of this dataset, the manuscript is still under revision.</p> <p>This dataset contains mouse T2 weighted MRI data, and manually and automated segmented ischemic stroke lesion masks for developing and evaluating a deep learning-based automated lesion segmentation. All files are in NIFTI format.<br> </p>
Generated Metastatic CT 3D Femurs with lesions segmentation 3/3
<p>This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al. </p> <p><strong>Three repositories are needed </strong>to form the full archive. When all are downloaded, they can be deflated with the following bash command : </p> <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre> <p>see the Generated Metastatic CT 3D Femurs with lesions segmentation 1/3 for information.</p>
Generated Metastatic CT 3D Femurs with lesions segmentation 2/3
<p>This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al. </p> <p><strong>Three repositories are needed </strong>to form the full archive. When all are downloaded, they can be deflated with the following bash command : </p> <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre> <p> </p> <p>Generated CT Scans are organized in two directories: one for files generated with DDPM this one) and one without DDPM.</p> <p>├── Generated_3DCT_Metastatic_Femurs_with_DDPM <br>│ ├── img<br>│ ├── lbl<br>│ └── msk</p> <p>└── Generated_3DCT_Metastatic_Femurs_NO_DDPM<br> ├── img<br> ├── lbl<br> └── msk</p> <p>The content of each directory is as follows: </p> <ul> <li>img : the generated 3D CT scan (nii.gz format) from the 26 healthy femurs</li> <li>lbl : the lesions segmentation (nii.gz format) of the generated 3D CT scan</li> <li>msk : the segmentations of the 26 healthy femurs (nii.gz)</li> </ul> <p>A given filename corresponds to in img and lbl directories :</p> <ul> <li>./lbl/MEK03les0MEK14.nii.gz is the lesions segmentation of ./img/MEK03les0MEK14.nii.gz.</li> <li>./lbl/MEK03les197MEK28.nii.gz is the lesions segmentation of ./img/MEK03les197MEK28.nii.gz</li> </ul> <p>But in msk, the corresponding femur mask for these examples is ./msk/MEK03.nii.gz</p> <p> </p>
Data from: Use of anisotropy, 3D segmented atlas, and computational analysis to identify gray matter subcortical lesions common to concussive injury from different sites on the cortex
Traumatic brain injury (TBI) can occur anywhere along the cortical mantel. While the cortical contusions may be random and disparate in their locations, the clinical outcomes are often similar and difficult to explain. Thus a question that arises is, do concussions at different sites on the cortex affect similar subcortical brain regions? To address this question we used a fluid percussion model to concuss the right caudal or rostral cortices in rats. Five days later, diffusion tensor MRI data were acquired for indices of anisotropy (IA) for use in a novel method of analysis to detect changes in gray matter microarchitecture. IA values from over 20,000 voxels were registered into a 3D segmented, annotated rat atlas covering 150 brain areas. Comparisons between left and right hemispheres revealed a small population of subcortical sites with altered IA values. Rostral and caudal concussions were of striking similarity in the impacted subcortical locations, particularly the central nucleus of the amygdala, laterodorsal thalamus, and hippocampal complex. Subsequent immunohistochemical analysis of these sites showed significant neuroinflammation. This study presents three significant findings that advance our understanding and evaluation of TBI: 1) the introduction of a new method to identify highly localized disturbances in discrete gray matter, subcortical brain nuclei without postmortem histology, 2) the use of this method to demonstrate that separate injuries to the rostral and caudal cortex produce the same subcortical, disturbances, and 3) the central nucleus of the amygdala, critical in the regulation of emotion, is vulnerable to concussion.
ASSESSMENT OF TRISTETRAPROLIN EXPRESSION IN LESIONAL SKIN OF PATIENTS WITH NON SEGMENTAL VITILIGO
ClinicalTrials.gov study NCT05990309. IPD Sharing: UNDECIDED. Countries: 0. Publications: 4.
Virtual Lesion Segmentation and Mandibular Ameloblastoma Radiographic Safety Margin
ClinicalTrials.gov study NCT07306962. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Use of anisotropy, 3D segmented atlas, and computational analysis to identify gray matter subcortical lesions common to concussive injury from different sites on the cortex
Open the record for dataset details and reuse information.
Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 1
<p>This archive contains the part 1 of Shift Benchmark on Multiple Sclerosis lesion segmentation data. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. This part is the MSSEG data collected in the digital repository of the OFSEP Cohort provided in the context of the MICCAI 2016 and 2021 challenges. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. Part 2 of the data is available <a href="https://zenodo.org/record/7051692">here</a>. To find out more about the Shifts Project, please visit https://shifts.ai .</p>
Efficacy and Safety Comparison of the Open and Endovascular Surgical Methods for the Treatment of Long Atherosclerotic Lesions of the Femoral-popliteal Segment Below the Knee, TASC D in Patients With
ClinicalTrials.gov study NCT04583436. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of an Automatic Segmentation Software (Pixyl.Neuro) to Track Lesions in Multiple Sclerosis Patients Via Cerebral MRI
ClinicalTrials.gov study NCT03438357. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy and Safety Comparison of the Open Surgical and Endovascular Methods for the Treatment of Long Atherosclerotic Lesions of the Femoral-popliteal Segment Above the Knee, TASC II, Type D.
ClinicalTrials.gov study NCT04588571. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Inter-observer Variability in the Segmentation of Prostate Tumour Lesions Using Multiparametric MRI (VARIOP)
ClinicalTrials.gov study NCT05996289. IPD Sharing: YES. Countries: 1. Publications: 0.
Instantaneous Wave-Free Ratio and Fractional Flow Reserve for the Assessment of Non Culprit Lesions in Patients With ST-segment Elevation Myocardial Infarction
ClinicalTrials.gov study NCT02869906. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Transplantation of Autologous Non-Cultured Extracted Hair Follicle Outer Root Sheath Cell Suspension and Mini Punch Graft for Stable Non-Segmental Vitiligo Lesions
ClinicalTrials.gov study NCT06619184. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy and Safety Comparison of the Endovascular and the Hybrid Methods for the Treatment of Prolonged Atherosclerotic Lesions of the Femoral-popliteal Segment Above the Knee, TASC II, Type D
ClinicalTrials.gov study NCT04590131. IPD Sharing: NO. Countries: 1. Publications: 0.
Next Generation Sequencing of zebrafish V2a interneurons in spinal segments rostral to the lesion after spinal cord injury
GEO Series GSE213081. Danio rerio. 32 samples. Type: Expression profiling by high throughput sequencing.
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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)
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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.