한빛사논문
- 조회421
Oh, Namkee MDa; Kim, Jae-Hun PhDb; Rhu, Jinsoo MD, PhDa; Jeong, Woo Kyoung MD, PhDb; Choi, Gyu-Seong MD, PhDa; Kim, Jong Man MD, PhDa; Joh, Jae-Won MD, PhDa
aDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
bDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
These authors contributed equally as first authors: Namkee Oh, Jae-Hun Kim.
*Corresponding authors: J. Rhu, W K. Jeong
Abstract
Background:
This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP)
Materials and Methods:
Living liver donors underwent MRCP using gradient and spin echo technique followed by three-dimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for deep learning process. Data were divided into training and test sets at a 9:1 ratio. Performance was assessed using the Dice Similarity Coefficient (DSC) to compare the model’s segmentation with the manually labeled ground truth.
Results:
The study incorporated 250cases. There was no difference in the baseline characteristics between the train set (n=225) and test set (n=25). The overall mean DSC was 0.80±0.20 between the ground truth and inference result. The qualitative assessment of the model showed relatively high accuracy especially for common bile duct (88%), common hepatic duct (92%), hilum (96%), right hepatic duct (100%) and left hepatic duct (96%), while the third order branch of right hepatic duct (18.2%) showed low accuracy.
Conclusion:
The developed automated segmentation model for biliary structures, utilizing MRCP data and deep learning techniques, demonstrated robust performance and holds potential for further advancements in automation
논문정보
- Research article
- 2024년 01월 (BRIC 등록일 2024-02-23)
- 국내 연구진
- 의약학 > 종양의학
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