Deep MRI glioma segmentation via multiple guidances and hybrid enhanced-gradient cross-entropy loss. (15th June 2022) - Zhang, Jinjing; Zhao, Lijun; Zeng, Jianchao; Qin, Pinle; Wang, Yanbo; Yu, Xiaoqing
Abstract: The low accuracy of MR image segmentation is often caused by blurred glioma region boundaries and intensity inhomogeneity as well as class-imbalance problems, which greatly influences glioma quantitative analysis. To resolve these problems, we propose a Deep Multiple Guidances based Glioma Segmentation Network (DMGSN), which is designed according to an observation of hierarchical structure of glioma region. In DMGSN, Glioma Sub-regions Prediction (GSP) block fuses guidance features from Whole Glioma Prediction (WGP) block and Glioma Boundary Prediction (GBP) block by importance ranking fusion module, which reduces redundancy among guidance features. Specifically, the WGP block is responsible to generate a whole glioma guidance map, which provides a key clue to exclude non-glioma regions for glioma sub-regions segmentation. Meanwhile, we introduce GBP block to estimate glioma sub-regions contours, whose multi-scale feature maps are added into GSP decoding path to strengthen segmentation features around glioma boundaries. Besides, hybrid enhanced-gradient cross-entropy loss regularizes DMGSN training, which efficiently alleviates class-imbalance problem. Large numbers of experimental results have shown that the proposed DMGSN has superior performances against many state-of-the-art glioma segmentation methods in terms of complete dice, core dice and enhance dice. Highlights: Glioma segmentation network is designed based on glioma hierarchical structure. Whole glioma prediction is proposed to reduce wrongly segmented points. Glioma boundary prediction is introduced to provide semantic glioma contour. Importance ranking fusion is introduced to reduce feature redundancy. Our hybrid enhanced-gradient cross-entropy loss can solve class-imbalance problem.