A Two-Stage Special Feature Deep Fusion Network with Spatial Attention for Hippocampus Segmentation

被引：0

作者：

Cai, Zhengwei ^{[1
]}

Wang, Shaoyu ^{[1
]}

Chen, Qiang ^{[1
]}

Lin, Runlong ^{[1
]}

Hu, Yun ^{[1
]}

Zhu, Yian ^{[1
]}

机构：

[1] Donghua Univ, Sch Comp Sci & Technol, Shanghai, Peoples R China

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SOFTWARE ENGINEERING (ICICSE 2021) | 2021年

基金：

中国国家自然科学基金;

关键词：

deep learning; two-stage network; u-net; semantic segmentation; hippocampus;

D O I：

10.1109/ICICSE52190.2021.9404111

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper proposes a method for automatically segmenting the hippocampus by a two-stage special feature deep fusion network based on spatial attention. The method extracts specific features of different resolutions to supplement the image detail information which are important for semantic segmentation and are partially lost in the downsampling process. At the same time, the spatial attention mechanism is used to solve the imbalance between the hippocampus and the background. The verification results on the NITRC dataset show that the method has achieved higher quality hippocampal segmentation results than U-Net++.

引用

页码：103 / 106

页数：4

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