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282
datasets available to search
ShareScore release 0.7.1
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282 results for “image segments”
Toward an Automated Method of Abdominal Fat Segmentation of MR Images
ClinicalTrials.gov study NCT01228968. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids
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Data from: Segmentation of laterally symmetric overlapping objects: application to images of collective animal behavior
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Data from: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain
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Data from: A novel mouse segmentation method based on dynamic contrast enhanced micro-CT images
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EP300 knock-out image segmentation
GEO Series GSE1054. Homo sapiens. 48 samples. Type: Expression profiling by array.
Comet assay annotation data for comet segmentation and scoring (1037 comet assay images)
<p>Comet assay annotation images used in the following paper</p> <p>Hong, Y., Han, HJ., Lee, H. <em>et al.</em> Deep learning method for comet segmentation and comet assay image analysis. <em>Sci Rep</em> <strong>10, </strong>18915 (2020). https://doi.org/10.1038/s41598-020-75592-7</p> <p>DeepComet online service website: <strong>https://ad3.io/</strong></p> <p>sign up->dashboard->APPs->Deep Comets->new job</p> <p> </p> <p>Two biology researchers who were experienced in the comet assay for over half a year under the guidance of a toxicology expert in the assay annotated the images independently and compared each other’s work to ensure consistency. Here, the <strong>VGG Image Annotator sofware</strong> was used to annotate the images manually. We annotated the intact comet heads without a tail using a circular region tool that outputs the center and radius of the circle. Further, comets with a tail and DNA damage were annotated with a polyline tool, where the frst dot was marked at the center of a comet head, and the second dot was marked at the bottom of the comet head. Tese two points were used to identify the head of a comet. Subsequently, dots were marked along the boundary of the comet tail counterclockwise from the second dot. Te total number of dots were at least eight per each comet.</p> <p>Comets can be sorted into non-ghost cells with a distinct head and tail, and ghost cells with a small or no nucleoid head and a broad tail. The ghost cells are usually classifed as the highest degree of DNA damage by visual scoring. Until now, there is a controversy over the cause of ghost cells and how they should be analyzed appropriately. Therefore, our method is designed to classify these two kinds of cells, so one can use some custom analysis techniques for ghost cells.</p> <p>The comets in our datasets were all classifed as non-ghost or ghost cells. Furthermore, we assigned tags to the comets if they were overlapped or located at the boundary of an image (i.e., only a part of the comet is visible). After manual annotation with dots and tags (in the red box), each dotted contour was flled up to give mask images.</p>
Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images
<p>Data for the Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images paper</p>
Data and Codes for: Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis
<p>Phase segmentation is a crucial step in X-ray computed tomography (CT) for image-based analysis (CT-IBA) to derive soil and root information. How segmentation uncertainty (SU) affects CT-IBA of vegetated soil has never been explored. The enclosed data and codes are used to assist the analysis of SU quantification and propagation in the journal paper published in Plant & Soil. The title of the paper is Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis. </p>
A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks
<p>Training and testing samples used for BME-X model: testing_data.hdf5, training_data.hdf5</p> <p>Templates for histogram matching before model testing: Template_T?_*.???</p> <p>Testing image: test_24m.???</p>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part III)
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} }</pre> </blockquote>
Maize Root Domain Shift Image Datasets, Segmentation Models, and RhizoVision Explorer Settings
<p>This .zip file contains the following: 1) a .csv file used for RhizoVision Explorer settings, 2) root segmentation .pkl models developed using RootPainter, and 3) folders of maize root images collected in the field and greenhouse experiment that were used to train models and that were manually annotated to generate ground-truth datasets.</p> <p>For clarification, the segmentation model file named "000053_1679678936_V7_2021_tiled.pkl" is the growth stage-specific V7 field model. The segmentation model file named "000040_1679498247_R2_2021_tiled.pkl" is the growth stage-specific R2 field model. The segmentation model file named "000022_1679880442_V7plusR2_2021_tiled.pkl" is the fine-tuned V7+R2 field model. The segmentation model file named "000059_1687007135_V7R2_Tiled_Combined_2021.pkl" is the combined V7+R2 field model. The segmentation model file named "000068_1687361521_V7extendedR2_tiled_2021.pkl" is the extended V7+R2 field model. The segmentation model file named "000027_1681159536_GH_R2_tiled.pkl" the R2 greenhouse model. The segmentation model file named "000059_1680366870_GH_V9_tiled.pkl" is the V9 greenhouse model.</p>
Med-ReLU: A Hybrid Activation Function Tailored for Deep Artificial Neural Networks in Medical Image Segmentation without Parameter Tuning
<p>Background: Deep learning (DL) is derived from the domain of Artificial Neural Network (ANN). It makes one of the most important elements of deep learning algorithms. Deep learning segmentation models are based on layer-by-layer convolution learning attribute representation directed by forward and backward propagation. Throughout the process vital role is played by appropriately chosen activation function (AF) in order to guarantee the robustness of the model learning. However, the existing activation functions are either ineffective in addressing the vanishing gradient problem or get burdened with multiple parameters that need to be manually tuned. Moreover, the current research on activation function design mainly focuses on classification tasks using natural images from the MNIST, CIFAR-10 and CIFAR-100 datasets. Therefore,Med-ReLU as a novel activation function for medical image segmentation, is proposed. The proposed activation function avoids deep learning models from the attacks of dead neurons or from the vanishing gradient problems. Method: Med-ReLU is a hybrid activation function that combines the property of two activation functions of ReLU and Softsign. For positive inputs, Med-ReLU utilizes the linear property just like ReLU to produce an output without vanishing gradient. The negative inputs converge in polynomial ways towards their asymptotes as property of the softsign AF that ensures robust training processing without the problem of dead neurons that rarely activate across the entire training dataset. Results: The training performance and segmentation accuracy of Med-ReLU have been investigated. The proposed function has demonstrated stable training and does not suffer from over-fitting. Hence, Med-ReLU has consistently outperformed the existing state-of-art activation functions in medical image segmentation tasks. Conclusion: Med-ReLU has been designed as a parameter-free activation function for DL image segmentation tasks. This activation function is easy-to-implement on complex and deep learning models. The utility of this research lies in affirming the impact of Med-ReLU on different Artificial Neural Network architectures and for various kinds of anomaly addressing tasks.</p>
Intelligent Segmentation Algorithm of Ultrasonic Image
ClinicalTrials.gov study NCT06646679. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Study of Imaging and Endoplant-related Mechanical Complications After Long Segment Fixation and Fusion of Degenerative Lumbar Scoliosis (DLS)
ClinicalTrials.gov study NCT04960423. IPD Sharing: NO. Countries: 1. Publications: 0.
Ex Vivo Effect of Surfactant Protein D on Pulmonary Cells in Patients With Asthma and Pilot Study to Assess Local Inflammation Induced by Segmental Allergen Challenge by Magnetic Resonance Imaging
ClinicalTrials.gov study NCT01378624. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Prospective Multicenter Clinical Study on the Long-term Prognosis of Patients With ST-segment Elevation Myocardial Infarction Using Cardiac Magnetic Resonance Imaging.
ClinicalTrials.gov study NCT07057492. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Fully Automated Pipeline for the Detection and Segmentation of Non-Small Cell Lung Cancer (NSCLC) on CT Images
ClinicalTrials.gov study NCT04164186. IPD Sharing: NO. Countries: 1. Publications: 0.
Endometriosis of the Recto-sigmoid: MRI (Magnetic Resonance Imaging) Criteria Predictive of Shaving or Segmental Resection
ClinicalTrials.gov study NCT03931603. IPD Sharing: NO. Countries: 1. Publications: 0.
Histological and Imaging Assessment of the Structural Characteristics of the Femoro-popliteal Arterial Segment
ClinicalTrials.gov study NCT03847597. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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.