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Found 4 papers, 1 papers with code
Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI
In this paper, we propose a new model that integrates prior knowledge from different contrasts for red nucleus segmentation.
How precise are performance estimates for typical medical image segmentation tasks?
An important issue in medical image processing is to be able to estimate not only the performances of algorithms but also the precision of the estimation of these performances.
Effect of Prior-based Losses on Segmentation Performance: A Benchmark
To enforce anatomical plausibility, recent research studies have focused on incorporating prior knowledge such as object shape or boundary, as constraints in the loss function.
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks.
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