基于多尺度特征和反向注意力的肝脏肿瘤自动分割方法

张瑞; 唐乔湛; 李斯卉; 宋江玲

doi:10.16152/j.cnki.xdxbzr.2023-06-007

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基于多尺度特征和反向注意力的肝脏肿瘤自动分割方法

西北大学学报110周年刊庆 | 更新时间：2024-01-03

- 基于多尺度特征和反向注意力的肝脏肿瘤自动分割方法
- Automatic liver tumor segmentation using multi-scale features and reverse attention
- 西北大学学报（自然科学版） 2023年53卷第6期页码：964-973
- 作者机构：
  
  西北大学　医学大数据研究中心，陕西　西安　710127
- 作者简介：
  
  [ "张瑞，博士，教授，博士生导师，“陕西省特支计划”科技创新领军人才，陕西省医学数据分析挖掘与智能应用创新团队负责人，微系统可靠性分析与仿真陕西省高校工程研究中心主任，西北大学医学大数据研究中心主任，西北大学数学学院院长。分别获得西安交通大学应用数学专业理学博士学位与新加坡南洋理工大学电子电气工程专业工学博士学位。先后赴美国伊利诺伊大学数学系、美国哈佛医学院及麻省总医院访问交流。目前从事机器学习理论与算法、医学数据分析与处理、神经计算建模等方向的教学与科研工作。已在国内外重要杂志和国际学术会议上发表学术论文80余篇，主持国家级省部级等科研项目9项，以第一完成人获陕西省科学技术奖二等奖1项。" ]
- 基金信息：
  
  国家自然科学基金(12071369;62006189);陕西省自然科学基金(2021JQ-430;2023-JC-QN-0028);中国博士后科学基金(2022M722580)
- DOI：10.16152/j.cnki.xdxbzr.2023-06-007
  中图分类号：
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张瑞, 唐乔湛, 李斯卉, 等. 基于多尺度特征和反向注意力的肝脏肿瘤自动分割方法[J]. 西北大学学报（自然科学版）, 2023,53(6):964-973.

ZHANG Rui, TANG Qiaozhan, LI Sihui, et al. Automatic liver tumor segmentation using multi-scale features and reverse attention[J]. Journal of Northwest University (Natural Science Edition), 2023,53(6):964-973.
张瑞, 唐乔湛, 李斯卉, 等. 基于多尺度特征和反向注意力的肝脏肿瘤自动分割方法[J]. 西北大学学报（自然科学版）, 2023,53(6):964-973. DOI： 10.16152/j.cnki.xdxbzr.2023-06-007.

ZHANG Rui, TANG Qiaozhan, LI Sihui, et al. Automatic liver tumor segmentation using multi-scale features and reverse attention[J]. Journal of Northwest University (Natural Science Edition), 2023,53(6):964-973. DOI： 10.16152/j.cnki.xdxbzr.2023-06-007.

摘要

肝脏肿瘤分割旨在定位肝脏肿瘤区域，以辅助医生进行精准诊治。鉴于深度学习能自动学习医学图像中复杂的特征和结构，已成为肝脏肿瘤分割的主流方法之一。但肝脏肿瘤的大小、形态存在显著差异及边缘模糊等问题，限制了深度学习模型的分割性能。基于此，该文提出了一种融合多尺度特征和反向注意力机制的深度网络，并用于肝脏肿瘤的自动分割。具体地，基于U-Net模型的框架，分别设计了多尺度特征提取模块和基于深度监督的反向注意力模块，使得网络能根据分割目标的大小自适应地选择不同尺度的特征，并引导网络关注分割目标的边缘特征，进而提高网络的边缘分割能力。此外，设计了一种新的混合损失，以解决医学图像分割中的类别不平衡问题。最后，在MICCAI2017 LiTS挑战赛数据集的数值实验结果表明，所提方法的Dice系数、平均对称表面距离ASSD分别为76.12%和3.25 mm。

Abstract

The objective of liver tumor segmentation is to pinpoint the region containing the liver tumor, aiding medical professionals in delivering precise diagnosis and treatment. Owing to its capacity to automatically learn intricate features and structures from medical images, deep learning has emerged as one of the mainstream approaches to liver tumor segmentation. Nonetheless, the size, shape, and blurred edges of liver tumor exhibit significant variations, which constrain the segmentation performance of deep learning models. Consequently, we introduce a deep network that incorporates multi-scale features and a reverse attention mechanism for the automatic segmentation of liver tumor. Specifically, drawing upon the U-Net model framework, this study designs a multi-scale feature extraction module and a reverse attention module based on deep supervision. This enables the network to adaptively select features of varying scales according to the size of the segmented target and direct the network’s attention towards the edge features of the segmented object, thereby enhancing the network’s edge segmentation capability. Furthermore, a novel hybrid loss is devised to address the issue of class imbalance in medical image segmentation.The recent numerical experiments conducted on the MICCAI2017 LiTS Challenge dataset have demonstrated that the proposed method achieves a Dice coefficient of 76.12% and an average symmetric surface distance (ASSD) of 3.25 mm.

关键词

肝脏肿瘤分割多尺度特征提取反向注意力

Keywords

liver tumor segmentationmulti-scale feature extractionreverse attention

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