# Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

arXiv: https://arxiv.org/abs/1808.06519 · HTML: https://mlsanity.com/paper/1808.06519/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Knowledge distillation from multi-modal to mono-modal segmentation networks](https://mlsanity.com/paper/2106.09564/) (2021)
- [Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation](https://mlsanity.com/paper/2107.09842/) (2021)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Complementary Information Mutual Learning for Multimodality Medical Image Segmentation](https://mlsanity.com/paper/2401.02717/) (2024)

## Abstract (arXiv)

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--. However, most of the existing retrospective datasets do not consist of FLAIR sequences. Existing missing modality imputation methods separate the process of imputation, and the process of segmentation. In this paper, we propose a method to link both modality imputation and segmentation using convolutional neural networks. We show that by jointly optimizing the imputation network and the segmentation network, the method not only produces more realistic synthetic FLAIR images from T1-w images, but also improves the segmentation of WMH from T1-w images only.