Deep MRI glioma segmentation via multiple guidances and hybrid enhanced-gradient cross-entropy loss
作者：
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.
摘要
•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.
论文关键词：Glioma segmentation,Deep neural network,Class-imbalance,Medical MR image,Segmentation loss
论文评审过程：Received 3 November 2021, Revised 25 December 2021, Accepted 22 January 2022, Available online 17 February 2022, Version of Record 21 February 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116608
