基于U-net卷积神经网络宫颈癌磁共振临床靶区和危及器官自动勾画的应用

doi:10.20047/j.issn1673-7210.2022.24.22

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Abstract Objective To establish the automatic sketching model of clinical target volume (CTV) and organs at risks (OARs) of cervical cancer based on magnetic resonance images using U-net convolutional neural network. Methods A total of 43 patients with stage ⅡB-ⅣA cervical cancer who underwent radical radiotherapy by large aperture magnetic resonance imaging in Shandong Cancer Hospital from April 2019 to December 2020 were enrolled. The tissue structure information of the patient’s magnetic resonance imaging was studied, and the region of interest (ROI) was delineated manually, including CTV and OARs (bladder, rectum, left femoral head, and right femoral head). A total of 43 patients were divided into training set (35 cases), validation set (4 cases) and test set (4 cases) by computer simple random sampling. U-net convolutional neural network was used to construct the training model, and the test set ROI was automatically sketching after verification. The time consumption and Dice similarity coefficient (DSC) value of manual sketching and automatic sketching were compared. Results The average automatic sketching time of patients was (44.5±0.6) s, which was shorter than that of manual sketching (2280.0±356.7) s, and the difference was statistically significant (P < 0.05). The DSC value of automatic and manual sketching: the rectum was (0.752±0.049); CTV was (0.831±0.038); bladder was (0.943±0.016); left femoral head was (0.894±0.009); right femoral head was (0.896±0.004). Conclusion U-net convolutional neural network combined with magnetic resonance images can accurately realize the automatic sketching of CTV and OARs, and improve the practical efficiency of clinical work.

Key words： Magnetic resonance Cervical cancer Automatic sketching U-net

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	LI Xia1 LIU Ya1 WANG Cong2 LI Zhenjiang2

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LI Xia1 LIU Ya1 WANG Cong2 LI Zhenjiang2. Application of automatic sketching of clinical target volume and organs at risk in cervical cancer based on U-net convolutional neural network[J]. 中国医药导报, 2022, 19(24): 98-102.

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https://www.yiyaodaobao.com.cn/EN/10.20047/j.issn1673-7210.2022.24.22 OR https://www.yiyaodaobao.com.cn/EN/Y2022/V19/I24/98

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