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An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation

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Abstract

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography–guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast’s tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method’s performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning–based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.

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Availability of Data and Material

The data are not publicly available at this time due to privacy/ethical restrictions.

Code Availability

The code that supports the findings of this study is available from the corresponding author upon reasonable request.

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Funding

This work is partly supported by grants from the National Natural Science Foundation of China (82202954, U20A201795, U21A20480, 61871374, 12126608), Beijing Natural Science Foundation (Z210008), Young S&T Talent Training Program of Guangdong Provincial Association for S&T, China (SKXRC202224), the Chinese Academy of Sciences Special Research Assistant Grant Program, the Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Project (ZY2022YL07), and the Guangzhou Science and Technology Plan (202102010264).

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Author notes

  1. Xiaokun Liang and Jingjing Dai share equal contribution to this work.

Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China

    Xiaokun Liang, Jingjing Dai, Xuanru Zhou, Lin Liu, Chulong Zhang & Yaoqin Xie

  2. Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA

    Yuming Jiang

  3. Department of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, China

    Na Li

  4. Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518118, China

    Tianye Niu

  5. Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China

    Tianye Niu

  6. Department of Radiation Therapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, China

    Zhenhui Dai & Xuetao Wang

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  1. Xiaokun Liang
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Contributions

All of the listed authors have participated actively in the study project. Xiaokun Liang and Jingjing Dai developed the design and conduct of the study. Xuanru Zhou, Zhenhui Dai, and Xuetao Wang led the data collection. Chulong Zhang aided in the data collection. Xuanru Zhou and Na Li annotated imaging data. Lin Liu and Yuming Jiang performed data analysis and writing. Tianye Niu and Yaoqin Xie performed investigation and writing. The first draft of the manuscript was written by Xiaokun Liang and all authors commented on previous versions of the manuscript. All authors participated in and approved the final submission.

Corresponding authors

Correspondence to Zhenhui Dai or Xuetao Wang.

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Ethics Approval

This retrospective study was approved by the ethics committee of The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (NO.BE2021-028–01).

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Informed consent was obtained from all individual participants included in the study.

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This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal’s policies, and we believe that neither the manuscript nor the study violates any of these.

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The authors declare no competing interests.

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Liang, X., Dai, J., Zhou, X. et al. An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation. J Digit Imaging 36, 923–931 (2023). https://doi.org/10.1007/s10278-023-00779-z

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  • DOI: https://doi.org/10.1007/s10278-023-00779-z

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