Deep learning-based two-step organs at risk 
auto-segmentation model for brachytherapy planning in parotid gland carcinoma

Zhen-Yu Li; Jing-hua Yue; Wei Wang; Wen-Jie Wu; Fu-gen Zhou; Jie Zhang; Bo Liu

doi:10.5114/jcb.2022.123972

eISSN: 2081-2841
ISSN: 1689-832X

Journal of Contemporary Brachytherapy

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6/2022
vol. 14

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abstract:

Original paper

Deep learning-based two-step organs at risk auto-segmentation model for brachytherapy planning in parotid gland carcinoma

Zhen-Yu Li

¹

,

Jing-hua Yue

²

,

Wei Wang

¹

,

Wen-Jie Wu

¹

,

Fu-gen Zhou

^{2, 3}

,

Jie Zhang

¹

,

Bo Liu

^{2, 3}

Department of Oral and Maxillofacial Surgery, Peking University School of Stomatology, Haidian District, Beijing, P.R. China
Image Processing Center, Beihang University, Beijing, P.R. China
Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, P.R. China

J Contemp Brachytherapy 2022; 14, 6: 527–535

DOI: https://doi.org/10.5114/jcb.2022.123972

Online publish date: 2022/12/30

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Purpose
Delineation of organs at risk (OARs) represents a crucial step for both tailored delivery of radiation doses and prevention of radiation-induced toxicity in brachytherapy. Due to lack of studies on auto-segmentation methods in head and neck cancers, our study proposed a deep learning-based two-step approach for auto-segmentation of organs at risk in parotid carcinoma brachytherapy.

Material and methods
Computed tomography images of 200 patients with parotid gland carcinoma were used to train and evaluate our in-house developed two-step 3D nnU-Net-based model for OARs auto-segmentation. OARs during brachytherapy were defined as the auricula, condyle process, skin, mastoid process, external auditory canal, and mandibular ramus. Auto-segmentation results were compared to those of manual segmentation by expert oncologists. Accuracy was quantitatively evaluated in terms of dice similarity coefficient (DSC), Jaccard index, 95th-percentile Hausdorff distance (95HD), and precision and recall. Qualitative evaluation of auto-segmentation results was also performed.

Results
The mean DSC values of each OAR were 0.88, 0.91, 0.75, 0.89, 0.74, and 0.93, respectively, indicating close resemblance of auto-segmentation results to those of manual contouring. In addition, auto-segmentation could be completed within a minute, as compared with manual segmentation, which required over 20 minutes. All generated results were deemed clinically acceptable.

Conclusions
Our proposed deep learning-based two-step OARs auto-segmentation model demonstrated high efficiency and good agreement with gold standard manual contours. Thereby, this novel approach carries the potential in expediting the treatment planning process of brachytherapy for parotid gland cancers, while allowing for more accurate radiation delivery to minimize toxicity.

keywords:

Deep learning-based two-step organs at risk auto-segmentation model for brachytherapy planning in parotid gland carcinoma

Zhen-Yu Li 1 , Jing-hua Yue 2 , Wei Wang 1 , Wen-Jie Wu 1 , Fu-gen Zhou 2, 3 , Jie Zhang 1 , Bo Liu 2, 3

automatic segmentation, organs at risk, parotid gland cancer, brachytherapy

Zhen-Yu Li

¹

,

Jing-hua Yue

²

,

Wei Wang

¹

,

Wen-Jie Wu

¹

,

Fu-gen Zhou

^{2, 3}

,

Jie Zhang

¹

,

Bo Liu

^{2, 3}