Efficient segmentation using domain adaptation for MRI-guided and CBCT-guided online adaptive radiotherapy

被引：4

作者：

Liu, Yuxiang ^{[1
]}

Yang, Bining ^{[1
]}

Chen, Xinyuan ^{[1
]}

Zhu, Ji ^{[1
]}

Ji, Guangqian ^{[1
]}

Liu, Yueping ^{[1
]}

Chen, Bo ^{[1
]}

Lu, Ningning ^{[1
]}

Yi, Junlin ^{[1
]}

Wang, Shulian ^{[1
]}

Li, Yexiong ^{[1
]}

Dai, Jianrong ^{[1
,2
]}

Men, Kuo ^{[1
,2
]}

机构：

[1] Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Canc Ctr, Natl Clin Res Ctr Canc, Beijing 100021, Peoples R China

[2] Chinese Acad Med Sci & Peking Union Med Coll, Natl Canc Ctr, Natl Clin Res Ctr Canc, Dept Radiat Oncol,Canc Hosp, Beijing 100021, Peoples R China

来源：

RADIOTHERAPY AND ONCOLOGY | 2023年 / 188卷

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

Domain adaptation; Auto-segmentation; Adaptive radiotherapy; Magnetic resonance imaging; Cone-beam computed tomography; COMPUTED-TOMOGRAPHY; IMAGES;

D O I：

10.1016/j.radonc.2023.109871

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Background: Delineation of regions of interest (ROIs) is important for adaptive radiotherapy (ART) but it is also time consuming and labor intensive.Aim: This study aims to develop efficient segmentation methods for magnetic resonance imaging-guided ART (MRIgART) and cone-beam computed tomography-guided ART (CBCTgART).Materials and methods: MRIgART and CBCTgART studies enrolled 242 prostate cancer patients and 530 nasopharyngeal carcinoma patients, respectively. A public dataset of CBCT from 35 pancreatic cancer patients was adopted to test the framework. We designed two domain adaption methods to learn and adapt the features from planning computed tomography (pCT) to MRI or CBCT modalities. The pCT was transformed to synthetic MRI (sMRI) for MRIgART, while CBCT was transformed to synthetic CT (sCT) for CBCTgART. Generalized segmentation models were trained with large popular data in which the inputs were sMRI for MRIgART and pCT for CBCTgART. Finally, the personalized models for each patient were established by fine-tuning the generalized model with the contours on pCT of that patient. The proposed method was compared with deformable image registration (DIR), a regular deep learning (DL) model trained on the same modality (DL-regular), and a generalized model in our framework (DL generalized).Results: The proposed method achieved better or comparable performance. For MRIgART of the prostate cancer patients, the mean dice similarity coefficient (DSC) of four ROIs was 87.2%, 83.75%, 85.36%, and 92.20% for the DIR, DL-regular, DL-generalized, and proposed method, respectively. For CBCTgART of the nasopharyngeal carcinoma patients, the mean DSC of two target volumes were 90.81% and 91.18%, 75.17% and 58.30%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. For CBCTgART of the pancreatic cancer patients, the mean DSC of two ROIs were 61.94% and 61.44%, 63.94% and 81.56%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively.Conclusion: The proposed method utilizing personalized modeling improved the segmentation accuracy of ART.(c) 2023 Elsevier B.V. All rights reserved.

引用

页数：9