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ShareScore release 0.9.0
Dataset results
14 results for “Cardiac segmentation”
A Study to Evaluate the Safety of 12 Weeks of Dosing With GW856553 and Its Effects on Inflammatory Markers, Infarct Size, and Cardiac Function in Subjects With Myocardial Infarction Without ST-segment
ClinicalTrials.gov study NCT00910962. IPD Sharing: YES. Countries: 9. Publications: 2.
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>
Immediate Unselected Coronary Angiography Versus Delayed Triage in Survivors of Out-of-hospital Cardiac Arrest Without ST-segment Elevation
ClinicalTrials.gov study NCT02750462. IPD Sharing: NO. Countries: 1. Publications: 5.
ST-segment Elevation Not Associated With Acute Cardiac Necrosis (LESTONNAC)
ClinicalTrials.gov study NCT05689970. IPD Sharing: NO. Countries: 1. Publications: 3.
Impact of Persistent Microvascular Obstruction by Cardiac Magnetic Resonance on Prognosis for ST-segment Elevation Myocardial Infarction
ClinicalTrials.gov study NCT06759532. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
Open the record for dataset details and reuse information.
A Deep Learning Based Cardiac Cine Segmentation Framework for Clinicians - Transfer Learning Application to 7T - Additional Data
<p><strong>Automatically Generated Segmentation Masks for Data Science Bowl Cardiac Challenge Data</strong></p> <p>These segmentation masks have been automatically generated with the <a href="https://github.com/baiwenjia/ukbb_cardiac">ukbb_cardiac</a> network by Bai et al. (2018, <a href="https://doi.org/10.1186/s12968-018-0471-x">doi:10.1186/s12968-018-0471-x</a>). In order to reproduce the data cleaning and conversion: download the data from <a href="https://www.kaggle.com/c/second-annual-data-science-bowl/data">Kaggle</a> and follow the data curation and conversion steps outlined in: <a href="https://github.com/chfc-cmi/cmr-seg-tl">cmr-seg-tl</a> and the associated publication (Link to be added).</p> <p>As this is a derived dataset please abide by the data use rules of the original dataset at kaggle and provide proper citation to the original data:</p> <blockquote> <p>The data for the Data Science Bowl is available for research and academic pursuits. Please cite as ‘Data Science Bowl Cardiac Challenge Data’.</p> </blockquote> <p>Please also cite the Bai et al. article for the algorithm and our publication for the data curation.</p>
Effect of Nicorandil on Cardiac Sympathetic Nerve for the Patients of Acute ST Segment Elevation Myocardial Infarction
ClinicalTrials.gov study NCT04826497. IPD Sharing: NO. Countries: 0. Publications: 7.
Straightened segmentation in 4D cardiac CT: A practical method for multiparametric characterization of the landing zone for transcatheter pulmonary valve replacement
<p>Supplementary files (8 videos, 2 tables) for ''Straightened segmentation in 4D cardiac CT: A practical method for multiparametric characterization of the landing zone for transcatheter pulmonary valve replacement''</p>
Effects of Nicorandil on Cardiac Infarct Size in Patients With ST-segment Elevation Acute Myocardial Infarction
ClinicalTrials.gov study NCT02449070. 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.
Development and Validation of a Cardiac Magnetic Resonance-Based Multimodal Deep Learning Model for Long-Term Outcome Prediction in ST-Segment Elevation Myocardial Infarction
ClinicalTrials.gov study NCT07277400. IPD Sharing: NO. Countries: 1. Publications: 0.
RAW DATA - An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar - RAW DATA
<p>Raw data (original images, labelled masks) from "An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar"; https://doi.org/10.1016/j.cmpb.2022.107321</p> <p> </p>
Dataset related to the article " Automated Left and Right Ventricular Chamber Segmentation in Cardiac Magnetic Resonance Images Using Dense Fully Convolutional Neural Network"
<p>This record contains raw data related to the article " Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network"</p> <p><br> Background and objective: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiacfunction evaluation and diagnosis. In order to have a common reference for comparing segmentation algorithms, several CMR image datasets were made available, but in general they do not include the most apical and basal slices, and/or gold standard tracing is limited to only one of the two ventricles, thus not fully corresponding to real clinical practice. Our aim was to develop a deep learning (DL) approach for automated segmentation of both RV and LV chambers from short-axis (SAX) CMR images, reporting separately the performance for basal slices, together with the applied criterion of choice.<br> Method: A retrospectively selected database (DB1) of 210 cine sequences (3 pathology groups) was considered: images (GE, 1.5 T) were acquired at Centro Cardiologico Monzino (Milan, Italy), and end-diastolic (ED) and end-systolic frames (ES) were manually segmented (gold standard, GS). Automatic ED and ES RV and LV segmentation were performed with a U-Net inspired architecture, where skip connections were redesigned introducing dense blocks to alleviate the semantic gap between the U-Net encoder and decoder. The proposed architecture was trained including: A) the basal slices where the Myo surrounded<br> the LV for at least the 50% and all the other slice; B) all the slices where the Myo completely surrounded the LV. To evaluate the clinical relevance of the proposed architecture in a practical use case scenario, a graphical user interface was developed to allow clinicians to revise, and correct when needed, the automatic segmentation. Additionally, to assess generalizability, analysis of CMR images obtained in 12 healthy volunteers (DB2) with different equipment (Siemens, 3T) and settings was performed.<br> Results: The proposed architecture outperformed the original U-Net. Comparing the performance on DB1 between the two criteria, no significant differences were measured when considering all slices together, but were present when only basal slices were examined. Automatic and manually-adjusted segmentation<br> performed similarly compared to the GS (bias±95%LoA): LVEDV -1±12 ml, LVESV -1±14 ml, RVEDV 6±12 ml, RVESV 6±14 ml, ED LV mass 6±26 g, ES LV mass 5±26 g). Also, generalizability showed very similar performance, with Dice scores of 0.944 (LV), 0.908 (RV) and 0.852 (Myo) on DB1, and 0.940 (LV), 0.880 (RV), and 0.856 (Myo) on DB2.<br> Conclusions: Our results support the potential of DL methods for accurate LV and RV contours segmentation and the advantages of dense skip connections in alleviating the semantic gap generated when high level features are concatenated with lower level feature. The evaluation on our dataset, considering separately the performance on basal and apical slices, reveals the potential of DL approaches for fast, accurate and reliable automated cardiac segmentation in a real clinical setting.<br> </p>
ScienceDex guides
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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.
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DANDI Archive for NWB datasets
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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.