基于方向约束强化学习的左心室内膜分割

曾安; 庞耀幸; 潘丹; 赵靖亮

doi:10.12052/gdutxb.230156

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基于方向约束强化学习的左心室内膜分割

计算机科学与技术 | 更新时间：2026-05-27

- 基于方向约束强化学习的左心室内膜分割
- Segmentation of Left Ventricular Endocardium Using Direction-constrained Reinforcement Learning
- 广东工业大学学报 2024年41卷第6期页码：60-68
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广东技术师范大学电子与信息学院,广东,广州,510665
- 作者简介：
  
  曾安(1978–)，女，教授，博士生导师，主要研究方向为图像处理、模式识别、人工智能，E-mail：zengan@gdut.edu.cn
  庞耀幸(2000–)，男，硕士研究生，主要研究方向为人工智能与疾病辅助诊断，E-mail：2603509767@qq.com
- 基金信息：
  
  国家自然科学基金资助项目(61976058，92267107);广东省重点领域研发计划项目(2021B0101220006);广东省科技计划项目(2019A050510041);广东省自然科学基金资助项目(2021A1515012300);广州市科技计划项目(202103000034)
- DOI：10.12052/gdutxb.230156
  中图分类号： TP389.1
- 纸质出版：2024
- 稿件说明：
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曾安, 庞耀幸, 潘丹, 赵靖亮. 基于方向约束强化学习的左心室内膜分割[J]. 广东工业大学学报, 2024, 41(6): 60-68.

Zeng An, Pang Yao-xing, Pan Dan, et al. Segmentation of Left Ventricular Endocardium Using Direction-constrained Reinforcement Learning[J]. 2024, 41(6): 60-68.
曾安, 庞耀幸, 潘丹, 赵靖亮. 基于方向约束强化学习的左心室内膜分割[J]. 广东工业大学学报, 2024, 41(6): 60-68. DOI： 10.12052/gdutxb.230156.

Zeng An, Pang Yao-xing, Pan Dan, et al. Segmentation of Left Ventricular Endocardium Using Direction-constrained Reinforcement Learning[J]. 2024, 41(6): 60-68. DOI： 10.12052/gdutxb.230156.

摘要

从心脏核磁共振成像中准确分割左心室内膜，进而得到左心室区域，是对心脏功能进行分析的重要步骤。针对强化学习通过定位左心室内膜边缘来进行分割时容易出现定位偏差而导致分割效果下降的问题，本文提出一种基于方向约束强化学习的左心室内膜分割方法。方法将分割任务划分为两个阶段：在第一阶段提取内膜的全局边缘特征；在第二阶段利用强化学习迭代定位内膜边缘点得到边缘，从而分割出左心室内膜。方法对智能体的定位方向进行约束，能减少定位偏差和重叠的情况从而提高分割精度。最后，在两个公共数据集自动心脏诊断挑战(Automatic Cardiac Diagnosis Challenge

ACDC)和Sunnybrook心脏MR左心室分割挑战(Sunnybrook Cardiac MR Left Ventricle Segmentation Challenge

Sunnybrook)上的实验结果显示：与其他方法相比，本文方法精度较高，其F1-score指标分别为

0.9482

、

0.9387

，平均垂直距离(Average Perpendicular Distance

APD)分别为

3.5863

、

4.9447

，能有效地分割左心室内膜。

Abstract

Accurate segmentation of the left ventricular endocardium from cardiac magnetic resonance imaging to obtain the left ventricular region is an important step in the analysis of cardiac function. It is noted that reinforcement learning is prone to localization deviation in left ventricula

r endocardium segmentation by locating the left ventricular endocardial edges

leading to a performance decrease in the segmentation. To address this

this paper proposes a direction-constrained reinforcement learning method for left ventricular endocardium segmentation

which divides the segmentation task into two stages. In the first stage

the proposed method extracts global edge features of the endocardium

and in the second stage

reinforcement learning is used to iteratively locate the endocardial edge points to obtain the edge

obtaining the segmented left ventricular endocardium. The proposed method constrains the direction of agent positioning

which can reduce the localization deviation and overlap

such that the segmentation accuracy can be improved. Finally

the experimental results on two public datasets

including the Automated Cardiac Diagnosis Challenge (ACDC) and Sunnybrook Cardiac MR Left Ventricle Segmentation Challenge (Sunnybrook)

show that the proposed method has higher accuracy than the compared methods. Specifically

the F1-score of the proposed method are

0.9482

and

0.9387

and the Average perpendicular distance (APD) are

3.5863

and

4.9447

which can effectively segment the left ventricular endocardium.

关键词

Keywords

references

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