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基于强化学习的B型主动脉夹层定位方法

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主动脉夹层分割中存在主动脉夹层与周围器官和血管的对比度低、夹层形态差异大以及背景噪声大等问题.针对以上问题,本文提出 一种基于强化学习的B型主动脉夹层定位方法,借助两阶段分割模型,使用深度强化学习执行第一阶段的主动脉定位任务,保证定位目标的完整性;在第二阶段,使用第一阶段的粗分割结果作为输入,得到精细的分割结果.为了提高一阶段分割结果的召回率(Recall),使定位结果更完整地包含分割目标,本文设计了基于Recall变化方向的强化学习奖励函数;同时,将定位窗口与视野窗口分离,减少分割目标缺失的情况.本文选取Unet、TransUnet、SwinUnet以及MT-Unet作为基准分割模型,通过实验验证,本文的两阶段分割流程结果中多数指标均优于基准结果,其中Dice指标分别提高1.34％、0.89％、27.66％和7.37％.综上,将本文的B型夹层定位方法加入分割流程,最终的分割精度较基准模型结果有所提升,对于分割效果较差的模型提升效果更显著.
Reinforcement learning-based method for type B aortic dissection localization
In the segmentation of aortic dissection,there are issues such as low contrast between the aortic dissection and surrounding organs and vessels,significant differences in dissection morphology,and high background noise.To address these issues,this paper proposed a reinforcement learning-based method for type B aortic dissection localization.With the assistance of a two-stage segmentation model,the deep reinforcement learning was utilized to perform the first-stage aortic dissection localization task,ensuring the integrity of the localization target.In the second stage,the coarse segmentation results from the first stage were used as input to obtain refined segmentation results.To improve the recall rate of the first-stage segmentation results and include the segmentation target more completely in the localization results,this paper designed a reinforcement learning reward function based on the direction of recall changes.Additionally,the localization window was separated from the field of view window to reduce the occurrence of segmentation target loss.Unet,TransUnet,SwinUnet,and MT-Unet were selected as benchmark segmentation models.Through experiments,it was verified that the majority of the metrics in the two-stage segmentation process of this paper performed better than the benchmark results.Specifically,the Dice index improved by 1.34％,0.89％,27.66％,and 7.37％for each respective model.In conclusion,by incorporating the type B aortic dissection localization method proposed in this paper into the segmentation process,the overall segmentation accuracy is improved compared to the benchmark models.The improvement is particularly significant for models with poorer segmentation performance.

Aortic dissectionTwo-stage segmentationReinforcement learningReward function

曾安、林先扬、赵靖亮、潘丹、杨宝瑶、刘鑫

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广东工业大学计算机学院(广州 510006)

广东技术师范大学电子与信息学院(广州 510665)

主动脉夹层 两阶段分割 强化学习 奖励函数

国家自然科学基金项目国家自然科学基金项目广东省重点领域研发计划项目广东省科技计划项目广东省自然科学基金项目广东省基础与应用基础研究区域联合基金项目广州市科技计划项目广州市科技计划项目广州市科技计划项目

61976058621020982021B01012200062019A0505100412021A15150123002022A1515140096202103000034202206010007202201010266

2024

10.7507/1001-5515.202309047

生物医学工程学杂志
四川大学华西医院　四川省生物医学工程学会

生物医学工程学杂志

CSTPCD北大核心
影响因子：0.432
ISSN：1001-5515
年,卷(期)：2024.41(5)