  Segmenting the fine structure of the mouse brain on magnetic resonance (MR)
images is critical for delineating morphological regions, analyzing brain
function, and understanding their relationships. Compared to a single MRI
modality, multimodal MRI data provide complementary tissue features that can be
exploited by deep learning models, resulting in better segmentation results.
However, multimodal mouse brain MRI data is often lacking, making automatic
segmentation of mouse brain fine structure a very challenging task. To address
this issue, it is necessary to fuse multimodal MRI data to produce
distinguished contrasts in different brain structures. Hence, we propose a
novel disentangled and contrastive GAN-based framework, named MouseGAN++, to
synthesize multiple MR modalities from single ones in a structure-preserving
manner, thus improving the segmentation performance by imputing missing
modalities and multi-modality fusion. Our results demonstrate that the
translation performance of our method outperforms the state-of-the-art methods.
Using the subsequently learned modality-invariant information as well as the
modality-translated images, MouseGAN++ can segment fine brain structures with
averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively,
achieving around +10% performance improvement compared to the state-of-the-art
algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image
synthesis and segmentation method, can be used to fuse cross-modality
information in an unpaired manner and yield more robust performance in the
absence of multimodal data. We release our method as a mouse brain structural
segmentation tool for free academic usage at https://github.com/yu02019.
