Auto-segmentation of surgical clips for target volume delineation in post-lumpectomy breast cancer radiotherapy.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Xin Xie, Peng Huang, Zhihui Hu, Yuhan Fan, Jiawen Shang, Ke Zhang, Hui Yan
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引用次数: 0

Abstract

Purpose: To develop an automatic segmentation model for surgical marks, titanium clips, in target volume delineation of breast cancer radiotherapy after lumpectomy.

Methods: A two-stage deep-learning model is used to segment the titanium clips from CT image. The first network, Location Net, is designed to search the region containing all clips from CT. Then the second network, Segmentation Net, is designed to search the locations of clips from the previously detected region. Ablation studies are performed to evaluate the impact of various inputs for both networks. The two-stage deep-learning model is also compared with the other existing deep-learning methods including U-Net, V-Net and UNETR. The segmentation accuracy of these models is evaluated by three metrics: Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and Average Surface Distance (ASD).

Results: The DSC, HD95 and ASD of the two-stage model are 0.844, 2.008 mm and 0.333 mm, while their values are 0.681, 2.494 mm and 0.785 mm for U-Net, 0.767, 2.331 mm and 0.497 mm for V-Net, 0.714, 2.660 mm and 0.772 mm for UNETR. The proposed 2-stage model achieved the best performance among the four models.

Conclusion: With the two-stage searching strategy the accuracy to detect titanium clips can be improved comparing to those existing deep-learning models with one-stage searching strategy. The proposed segmentation model can facilitate the delineation of tumor bed and subsequent target volume for breast cancer radiotherapy after lumpectomy.

在乳房肿瘤切除术后的乳腺癌放疗中,手术夹的自动分割用于靶体积描绘。
目的:建立一种用于乳房肿瘤切除术后乳腺癌放疗靶体积描绘的手术标记、钛夹自动分割模型。方法:采用两阶段深度学习模型对CT图像中的钛夹进行分割。第一个网络,定位网,被设计用来搜索包含CT所有片段的区域。然后设计第二种网络分割网,从先前检测到的区域中搜索片段的位置。消融研究是为了评估两个网络的不同输入的影响。将两阶段深度学习模型与现有的U-Net、V-Net和UNETR等深度学习方法进行了比较。这些模型的分割精度通过三个指标来评估:骰子相似系数(DSC)、95%豪斯多夫距离(HD95)和平均表面距离(ASD)。结果:两阶段模型的DSC、HD95和ASD分别为0.844、2.008 mm和0.333 mm, U-Net模型的DSC、HD95和ASD分别为0.681、2.494 mm和0.785 mm, V-Net模型的DSC、HD95和ASD分别为0.767、2.331 mm和0.497 mm, UNETR模型的DSC、HD95和ASD分别为0.714、2.660 mm和0.772 mm。提出的两阶段模型在四种模型中表现最好。结论:与现有的单阶段搜索模型相比,采用两阶段搜索策略可以提高钛夹的检测精度。所提出的分割模型可以为乳房肿瘤切除术后乳腺癌放疗的肿瘤床及后续靶体积的划定提供便利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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