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39 results for “Tissue segmentation”
Test Dataset for 3D semantic image segmentation of the Breast, Fibrograndular Tissue, and Breast Carcinoma
Open the record for dataset details and reuse information.
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 1 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the first part of 14 parts of the full dataset (1/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 10ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>Within this part, we also include the segmentation labels for each tissue.</p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 9 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the ninth part of 14 parts of the full dataset (9/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Trained Models for "Segmentation of human functional tissue units in support of a Human Reference Atlas"
<p>This repository contains all trained models for five algorithms as documented in the paper "Segmentation of human functional tissue units in support of a Human Reference Atlas".</p>
Data for "Segmentation of human functional tissue units in support of a Human Reference Atlas"
<p>This repository contains the data documented in the paper "Segmentation of human functional tissue units in support of a Human Reference Atlas".</p> <p>The directories contain:</p> <ul> <li>The HuBMAP and HPA image data, ground truth segmentation masks, and predicted segmentation masks, as documented in the paper. </li> <li>The source data for recreating the figures 3 and 4 in the paper.</li> </ul>
Data set for sub-millimetre MRI tissue class segmentation
<p>Sub-millimetre 7Tesla MRI image data set of the human brain for supervised training of algorithms to perform tissue class segmentation.</p> <p>The dataset contains preprocessed MRI images (co-registered + bias corrected) and corresponding ground truth labels.</p> <p>The dataset contains two different acquisitions:</p> <p>- MPRAGE dataset, based on 5 subjects, with T1w, PDw and T2w images</p> <p>- MP2RAGE dataset, based on 4 subjects, with inv1, inv2 and me gre images</p> <p> </p> <p>The following ground truth labels are provided:<br> [1] white matter<br> [2] grey matter<br> [3] cerebrospinal fluid<br> [4] ventricles<br> [5] subcortical<br> [6] vessels<br> [7] sagittal sinus</p> <p>Images are saved as nifti files and organized in BIDS format.</p> <p>This dataset is an extension of the following, initial dataset publication:</p> <p>* Dataset: A scalable method to improve gray matter segmentation at ultra high field MRI.</p> <p>The initial dataset is also available as a zenodo repository and can be downloaded from:<br> https://zenodo.org/record/1206163</p>
Data from: Automated segmentation of complex patterns in biological tissues: lessons from stingray tessellated cartilage
Introduction - Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods - Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results - Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.
micro-CT data and segmentation files for "The ant abdomen: the skeletomuscular and soft tissue anatomy of Amblyopone australis workers (Hymenoptera: Formicidae)"
<p>Two compressed (.zip) files are included. One (Amblyopone_australis_CASENT0753222_CT.zip) contains a directory of 2149 .tif files comprising the micro-CT dataset used in the study. The other (Amblyopone_australis_CASENT0753222_Segmentation.zip) contains a .ORSSession (ORS Dragonfly) file with the dataset and all segmentation labels created in the study. The segmentation labels are organized into multi-regions of interest (multiROIs) for organizational purposes.</p>
Assessing the Stability of Uterine Scar Tissue in Women With Previous History of Caesarean Section Using Multimodal Analyses of the Lower Uterine Segment Including Quantitative Sonography
ClinicalTrials.gov study NCT02827591. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Automated segmentation of complex patterns in biological tissues: lessons from stingray tessellated cartilage
Open the record for dataset details and reuse information.
Evaluation of Serum and Tissue Cathepsin L in Non-segmental Vitiligo Patients
ClinicalTrials.gov study NCT06261073. IPD Sharing: Not stated. Countries: 0. Publications: 4.
Cell atlas of the human ocular anterior segment: Tissue-specific and shared cell types
GEO Series GSE199013. Homo sapiens. 38 samples. Type: Expression profiling by high throughput sequencing.
Effect of NB-UVB on the Tissue Level of IL 15 and IL-15Rα in Active Non Segmental Vitiligo Cases.
ClinicalTrials.gov study NCT05316987. IPD Sharing: YES. Countries: 1. Publications: 0.
Transcriptome analysis of tubulointerstitial tissues of patients with focal segmental glomerulosclerosis
GEO Series GSE125779. Homo sapiens. 16 samples. Type: Expression profiling by array.
The influence of segmental copy number variation on tissue transcriptomes through development
GEO Series GSE16675. Mus musculus. 72 samples. Type: Expression profiling by array.
Profiling expression changes caused by a segmental aneuploid in maize meristem tissues
GEO Series GSE19212. Zea mays. 6 samples. Type: Expression profiling by array.
Transcriptome analysis of tubular tissues of focal segmental glomerulosclerosis patients
GEO Series GSE121211. Homo sapiens. 10 samples. Type: Expression profiling by array.
High resolution spatial trancrioptomics combined with deep learning-based image segmentation enables single-cell spatial profiling of archival FFPE kidney tissue from patients with idiopathic nephroti
GEO Series GSE300895. Homo sapiens. 3 samples. Type: Other.
Effects of temporal segmentation of meal composition on human adipose tissue transcriptome
GEO Series GSE118280. Homo sapiens. 90 samples. Type: Expression profiling by array.
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.