Abstract
BACKGROUND: Brain structure segmentation is of great value in diagnosing brain disorders, allowing radiologists to quickly acquire regions of interest and assist in subsequent analyses, diagnoses and treatment. Current brain structure segmentation methods are usually applied to magnetic resonance (MR) images, which provide higher soft tissue contrast and better spatial resolution. However, fewer segmentation methods are conducted on a positron emission tomography/magnetic resonance imaging (PET/MRI) system that combines functional and structural information to improve analysis accuracy. METHODS: In this paper, we explore a dual-modality image segmentation model to segment brain (18)F-fluorodeoxyglucose ((18)F-FDG) PET/MR images based on the U-Net architecture. This model takes registered PET and MR images as parallel inputs, and four evaluation metrics (Dice score, Jaccard coefficient, precision and sensitivity) are used to evaluate segmentation performance. Moreover, we also compared the proposed approach with other single-modality segmentation strategies, including PET-only segmentation and MRI-only segmentation. RESULTS: The experiments were conducted on the clinical head data of 120 patients, and the results show that the proposed algorithm accurately delineates brain volumes of interest (VOIs), achieving superior performance with 84.24%±1.44% Dice score, 74.36%±2.40% Jaccard, 84.33%±1.56% precision and 84.73%±1.56% sensitivity. Furthermore, compared with directly using the FreeSurfer toolkit, the proposed method reduced the segmentation time, which only needs 20 seconds to segment the whole brain for each patient. CONCLUSIONS: We present a deep learning-based method for the joint segmentation of anatomical and functional PET/MR images. Compared with other single-modality methods, our method greatly improved the accuracy of brain structure delineation, which shows great potential for brain analysis.