eISSN: 2081-2841
ISSN: 1689-832X
Journal of Contemporary Brachytherapy
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SCImago Journal & Country Rank

3/2021
vol. 13
 
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abstract:
Original paper

Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy

Hai Hu
1, 2
,
Qiang Yang
1, 2
,
Jie Li
2
,
Pei Wang
2
,
Bin Tang
2
,
Xianliang Wang
2
,
Jinyi Lang
2

1.
Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China
2.
Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
J Contemp Brachytherapy 2021; 13, 3: 325–330
Online publish date: 2021/05/13
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Introduction
Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning.

Material and methods
U-Net model was developed for applicator segmentation in CT images. Sixty cervical cancer patients with Fletcher applicator were divided into training data and validation data according to ratio of 50 : 10, and another 10 patients with Fletcher applicator were employed to test the model. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared.

Results
The averaged overall 10 test patients’ DSC, HD95, and reconstruction time were 0.89, 1.66 mm, and 17.12 s, respectively. The average tip error was 0.80 mm, and the average shaft error was less than 0.50 mm. The dosimetric differences between manual reconstruction and automatic reconstruction were 0.29% for high-risk clinical target volume (HR-CTV) D90%, and less than 2.64% for organs at risk D2cc at a scenario of doubled maximum shaft error.

Conclusions
We proposed a deep learning-based reconstruction method to localize Fletcher applicator in three-dimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.

keywords:

deep learning, applicator segmentation, brachytherapy, cervical cancer, dosimetric comparison

 
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