RadGenome-Anatomy is a large-scale chest radiograph dataset with anatomy labels obtained by projecting 3D CT masks into 2D radiographic space for 210 structures in 25,692 studies.
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.arXiv preprint arXiv:1809.04430, 2018
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A deep learning pipeline segments brain organs at risk from MRI with mean surface distances of 0.1-0.7 mm and 96% clinical acceptability on test data.
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RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection
RadGenome-Anatomy is a large-scale chest radiograph dataset with anatomy labels obtained by projecting 3D CT masks into 2D radiographic space for 210 structures in 25,692 studies.
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Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
A deep learning pipeline segments brain organs at risk from MRI with mean surface distances of 0.1-0.7 mm and 96% clinical acceptability on test data.