Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRI
This work addresses segmentation for medical imaging applications, but it is incremental as it compares existing methods without introducing new ones.
The study tackled multimodal liver segmentation in paired but unregistered T1 and T2-weighted MRI by comparing various deep learning strategies, finding that most methods achieved similar performances except for a multi-task approach that performed poorly.
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.