基于解剖结构感知的宫颈癌危及器官结直肠CT分割  

Anatomical Structure-Aware CT Segmentation of Organ-at-Risk Colorectum for Cervical Cancer

作　　者：张宇鑫 谢耀钦[1,2] 孙德宇 高玉华[3] 崔明 秦文健 ZHANG Yuxin;XIE Yaoqin;SUN Deyu;GAO Yuhua;CUI Ming;QIN Wenjian(Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;University of Chinese Academy of Sciences,Beijing 100049,China;Liaoning Cancer Hospital and Institute,Shenyang 110042,China)

机构地区：[1]中国科学院深圳先进技术研究院,深圳518055 [2]中国科学院大学,北京100049 [3]辽宁省肿瘤医院,沈阳110042

出　　处：《集成技术》2025年第2期13-23,共11页Journal of Integration Technology

基　　金：国家自然科学基金项目(U20A20373);深港肿瘤智能计算综合实验室项目(E3G111)。

摘　　要：宫颈癌是全球女性癌症死亡的主要原因之一,放射性治疗是治疗宫颈癌的常用方法。其中,近距离放射治疗通过将放射源直接置入靠近肿瘤的区域,能将高剂量的辐射集中在肿瘤部位,比其他放疗方法的适用性高。精确分割危及器官对准确估算放疗剂量和最大度地保护正常组织免受辐射损伤至关重要。然而,管状结构(如结直肠)的自动分割仍面临诸多挑战。例如:肠道的褶皱、运动伪影等因素会影响分割效果;近距离放射治疗中的放射源会降低CT影像的质量,影响分割的准确性。本文提出了一种基于中心线和距离图信息的宫颈癌管状危及器官分割方法,通过增强网络对解剖结构的学习,该方法能更好地识别管状器官的拓扑结构和其在人体内的空间关系,从而提高分割精度,优化放疗剂量分布。该文利用Dice相似度系数(DSC)、交并比(IoU)、召回率(Recall)和95%豪斯多夫距离(HD95)等指标分析了宫颈癌内放射治疗CT数据集的性能。实验结果表明本文方法的多数指标优于基线网络ResUNet。其中:DSC为71.58%,IoU为52.12%,Recall为79.03%,分别较ResUNet提高了11.29%、7.84%和12.70%;HD95为10.06,较ResUNet下降了1.76。由此表明本文方法能有效提高宫颈癌近距离放射治疗CT影像中结直肠的分割精度,减少复杂器官和影像质量对分割结果的影响。Cervical cancer is one of the leading causes of cancer-related death among women globally,and radiotherapy is a common treatment method for cervical cancer.Among the various radiation therapies,brachytherapy,which involves placing the radiation source directly into an area close to the tumor,can deliver a high dose of radiation directly to the tumor,making it more applicable compared to other radiation methods.Accurate segmentation of organs-at-risk is crucial for accurately estimating radiotherapy doses and maximizing protection of normal tissues from radiation damage.However,automatic segmentation of tubular structures,such as the colon and rectum,remains challenging.Factors such as intestinal folds and motion artifacts can affect the segmentation performance,and the presence of the radiation source in brachytherapy can degrade CT image quality,further impacting the segmentation results.This paper proposes a method for the segmentation of tubular organ-at-risk in cervical cancer based on centerline and distance map information.By enhancing the network’s understanding of anatomical structures,the method improves the identification of the topological structure of tubular organs and their spatial relationships within the human body,thus improving segmentation accuracy and optimizing radiotherapy dose distribution.Through experimental evaluation on a cervical cancer brachytherapy dataset,performance analysis was conducted using metrics such as Dice similarity coefficient(DSC),intersection over union(IoU),Recall,and 95%Hausdorff distance(HD95).The experimental results show that the proposed method outperforms the baseline network ResUNet in most metrics,specifically with a DSC of 71.58%,an IoU of 52.12%,a Recall of 79.03%,which improve by 11.29%,7.84%and 12.70%,respectively,compared to ResUNet.The HD95 is 10.06,which is a decrease of 1.76 compared to ResUNet.The results indicate that the proposed method effectively improves the segmentation accuracy of the colon and rectum in cervical cancer brachytherapy CT image

关 键 词：宫颈癌 危及器官 管状器官分割 CT图像 U-Net 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] R319[自动化与计算机技术—控制科学与工程]