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ShareScore release 0.9.0
Dataset results
9 results for “Lung Segmentation”
MRI Neonatal Lung Segmentation and 3D Morphologic Features
<p>We developed an ensemble of deep convolutional neural networks (2D-UNets) to perform automated neonatal lung segmentation from MRI sequences. A three-dimensional reconstruction is used to calculate MRI features for lung volume, shape, pixel intensity, and surface.</p> <p>In addition, ML Models for severity prediction of Bronchopulmonary Dysplasia (BPD) are implemented as an applied example of the use of MRI lung volumetric features for disease prognosis.</p> <p>This dataset comprises:</p> <ul> <li>Three pretrained 2D-UNet Models for Neonatal MRI Lung Segmentation.</li> <li>Resulting performances and features per MRI-sequence.</li> </ul> <p>See Publication:</p> <p>Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease (2023)</p> <p><a href="https://doi.org/10.1148/ryai.220239">https://doi.org/10.1148/ryai.220239</a></p>
Data and code from: Wide-Angle Lung Experiment Segmentation (WALES): A novel methodology for quantitative assessment of lung pathology in model systems
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COVID-19 CT Lung and Infection Segmentation Dataset
<p>This dataset contains 20 labeled COVID-19 CT scans. Left lung, right lung, and infections are labeled by two radiologists and verified by an experienced radiologist. <br> To promote the studies of annotation-efficient deep learning methods, we set up three segmentation benchmark tasks based on this dataset <a href="https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark">https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark</a>.</p> <p>In particular, we focus on learning to segment left lung, right lung, and infections using</p> <ul> <li>pure but limited COVID-19 CT scans;</li> <li>existing labeled lung CT dataset from other non-COVID-19 lung diseases;</li> <li>heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.</li> </ul>
Prognostic Value of Lung Ultrasound in ST Segment Elevation Acute Myocardial Infarction (LUS-AMI)
ClinicalTrials.gov study NCT04526535. IPD Sharing: NO. Countries: 1. Publications: 11.
Intra-operative Air Leak Management After Minimally Invasive Lung Segmental Resection
ClinicalTrials.gov study NCT06544200. IPD Sharing: NO. Countries: 1. Publications: 4.
Segmentation of Structural Abnormalities in Chronic Lung Diseases
ClinicalTrials.gov study NCT04760548. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effects of CSL324 in the Lung After Segmental Challenge
ClinicalTrials.gov study NCT05653713. IPD Sharing: YES. Countries: 1. Publications: 0.
Immune Treatment of Brain Metastasis of Lung Cancer Combined With Large-segmentation Precision Radiotherapy
ClinicalTrials.gov study NCT05584267. 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.
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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.