Consispace is a semantic-aware resampling method that uses an implicit neural network with ODE constraints and feature reweighting to achieve consistent axial voxel spacing while preserving anatomy and semantics, improving downstream segmentation.
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
citing papers explorer
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Towards Voxel Spacing Consistency for Medical Image Segmentation
Consispace is a semantic-aware resampling method that uses an implicit neural network with ODE constraints and feature reweighting to achieve consistent axial voxel spacing while preserving anatomy and semantics, improving downstream segmentation.
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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.