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

TL;DR

This paper introduces a joint deep learning approach to synthesize FLAIR images and segment white matter hyperintensities from T1 MRIs, addressing missing modality issues and improving segmentation accuracy.

Contribution

It proposes a unified CNN framework that simultaneously performs FLAIR synthesis and WMH segmentation, enhancing both tasks compared to separate methods.

Findings

Joint optimization improves FLAIR synthesis realism.

Enhanced WMH segmentation accuracy from T1 images.

Method outperforms existing separate imputation and segmentation approaches.

Abstract

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.