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

arXiv: https://arxiv.org/abs/2101.06979 · HTML: https://mlsanity.com/paper/2101.06979/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis](https://mlsanity.com/paper/1908.06912/) (2019)
- [Understanding and Improving Information Transfer in Multi-Task Learning](https://mlsanity.com/paper/2005.00944/) (2020)
- [A review: Deep learning for medical image segmentation using multi-modality fusion](https://mlsanity.com/paper/2004.10664/) (2020)
- [Semantic Segmentation using Adversarial Networks](https://mlsanity.com/paper/1611.08408/) (2016)
- [Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation](https://mlsanity.com/paper/1907.13590/) (2019)
- [EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection](https://mlsanity.com/paper/1806.05525/) (2018)
- [Liver lesion segmentation informed by joint liver segmentation](https://mlsanity.com/paper/1707.07734/) (2017)
- [I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation](https://mlsanity.com/paper/1908.02711/) (2019)

## Built on by (most cited first, 1 of 1)

- [FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation](https://mlsanity.com/paper/2205.11096/) (2022)

## Abstract (arXiv)

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.