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Found 3 papers, 3 papers with code
Localise to segment: crop to improve organ at risk segmentation accuracy
Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest.
Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image Synthesis
This, in contrast to previous methods, allows us to synthesize continuous, smooth, and high-quality longitudinal volumetric data with limited supervision.
RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data.
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