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Deep-Learning-Based Automatic Segmentation of Head and Neck Organs for Radiation Therapy in Dogs

Authors
 Jeongsu Park  ;  Byoungsu Choi  ;  Jaeeun Ko  ;  Jaehee Chun  ;  Inkyung Park  ;  Juyoung Lee  ;  Jayon Kim  ;  Jaehwan Kim  ;  Kidong Eom  ;  Jin Sung Kim 
Citation
 FRONTIERS IN VETERINARY SCIENCE, Vol.8 : 721612, 2021-09 
Journal Title
FRONTIERS IN VETERINARY SCIENCE
Issue Date
2021-09
Keywords
artificial intelligence ; deep-learning-based automatic segmentation ; dog head and neck ; head and neck cancer ; radiation therapy
Abstract
Purpose: This study was conducted to develop a deep learning-based automatic segmentation (DLBAS) model of head and neck organs for radiotherapy (RT) in dogs, and to evaluate the feasibility for delineating the RT planning. Materials and Methods: The segmentation indicated that there were potentially 15 organs at risk (OARs) in the head and neck of dogs. Post-contrast computed tomography (CT) was performed in 90 dogs. The training and validation sets comprised 80 CT data sets, including 20 test sets. The accuracy of the segmentation was assessed using both the Dice similarity coefficient (DSC) and the Hausdorff distance (HD), and by referencing the expert contours as the ground truth. An additional 10 clinical test sets with relatively large displacement or deformation of organs were selected for verification in cancer patients. To evaluate the applicability in cancer patients, and the impact of expert intervention, three methods-HA, DLBAS, and the readjustment of the predicted data obtained via the DLBAS of the clinical test sets (HA_DLBAS)-were compared. Results: The DLBAS model (in the 20 test sets) showed reliable DSC and HD values; it also had a short contouring time of ~3 s. The average (mean ± standard deviation) DSC (0.83 ± 0.04) and HD (2.71 ± 1.01 mm) values were similar to those of previous human studies. The DLBAS was highly accurate and had no large displacement of head and neck organs. However, the DLBAS in the 10 clinical test sets showed lower DSC (0.78 ± 0.11) and higher HD (4.30 ± 3.69 mm) values than those of the test sets. The HA_DLBAS was comparable to both the HA (DSC: 0.85 ± 0.06 and HD: 2.74 ± 1.18 mm) and DLBAS presented better comparison metrics and decreased statistical deviations (DSC: 0.94 ± 0.03 and HD: 2.30 ± 0.41 mm). In addition, the contouring time of HA_DLBAS (30 min) was less than that of HA (80 min). Conclusion: In conclusion, HA_DLBAS method and the proposed DLBAS was highly consistent and robust in its performance. Thus, DLBAS has great potential as a single or supportive tool to the key process in RT planning.
Files in This Item:
T202104095.pdf Download
DOI
10.3389/fvets.2021.721612
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jinsung(김진성) ORCID logo https://orcid.org/0000-0003-1415-6471
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/185428
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