A Tri-Attention fusion guided multi-modal segmentation network

作者：

Highlights：

• A novel correlation description block is introduced to discover the latent multi-source correlation between modalities.

• A constraint based on the correlation using KL divergence is proposed to aide the segmentation network to extract the correlated feature representation for a better segmentation.

• A tri-attention fusion strategy is proposed to recalibrate the feature representation along modality-attention, spatial-attention and correlation-attention paths.

• The first 3D multi-modal brain tumor segmentation network guided by tri-attention fusion is proposed.

摘要

•A novel correlation description block is introduced to discover the latent multi-source correlation between modalities.•A constraint based on the correlation using KL divergence is proposed to aide the segmentation network to extract the correlated feature representation for a better segmentation.•A tri-attention fusion strategy is proposed to recalibrate the feature representation along modality-attention, spatial-attention and correlation-attention paths.•The first 3D multi-modal brain tumor segmentation network guided by tri-attention fusion is proposed.

论文关键词：Multi-modality fusion,Correlation,Brain tumor segmentation,Deep learning

论文评审过程：Received 5 November 2020, Revised 7 July 2021, Accepted 1 November 2021, Available online 2 November 2021, Version of Record 28 February 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108417