MR-guided automatic whole-brain segmentation via deep learning technology based on integrated PET/MRI system

Motivation: Segmentation of brain tissues plays a significant role in quantifying and visualizing anatomical structures based on PET/MRI systems. Goal(s): However, most of the current methods are based on unimodal MRI but rarely combine structural and functional dual-modality information. Approach: In this paper, we proposed a dual-modality segmentation framework to achieve automatic and accurate segmentation for the whole brain. Results: The numerical experimental results demonstrate that the proposed method can incorporate multimodal information with the efficient and accurate segmentation performance achieved, allowing for better visualization and quantification results. Impact: We proposed a novel dual-modality whole-brain segmentation method based on PET and MR images that is beniificial to enrich the network features. Additionally, our method has reduced the segmentation time and could be implemented with other multimodal data.