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arXiv · 2101.06979

Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRI

Vincent Couteaux·Mathilde Trintignac·Olivier Nempont·Guillaume Pizaine·Anna Sesilia Vlachomitrou·Pierre-Jean Valette·Laurent Milot·Isabelle Bloch
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Abstract

We address the problem of multimodal liver segmentation in paired but unregistered T1 and T2-weighted MR images. We compare several strategies described in the literature, with or without multi-task training, with or without pre-registration. We also compare different loss functions (cross-entropy, Dice loss, and three adversarial losses). All methods achieved comparable performances with the exception of a multi-task setting that performs both segmentations at once, which performed poorly.

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Vincent Couteaux, Mathilde Trintignac, Olivier Nempont, Guillaume Pizaine, Anna Sesilia Vlachomitrou, Pierre-Jean Valette, Laurent Milot, Isabelle Bloch. 2021-01-18. Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRI. https://arxiv.org/abs/2101.06979

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