한빛사논문
- 조회268
성균관대학교 의과대학, 삼성서울병원
Oh, Namkee MDa; Kim, Jae-Hun PhDb; Rhu, Jinsoo MD, PhDa,*; Jeong, Woo Kyoung MD, PhDb,*; Choi, Gyu-Seong MD, PhDa; Man Kim, Jong 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
Namkee Oh and Jae-Hun Kim: These authors contributed equally as first authors.
*Corresponding authors: J. Rhu, W. K. Jeong
Abstract
Background: Precise preoperative assessment of liver vasculature and volume in living donor liver transplantation is essential for donor safety and recipient surgery. Traditional manual segmentation methods are being supplemented by deep learning (DL) models, which may offer more consistent and efficient volumetric evaluations.
Methods: This study analyzed living liver donors from Samsung Medical Center using preoperative CT angiography data between April 2022 and February 2023. A DL-based 3D residual U-Net model was developed and trained on segmented CT images to calculate the liver volume and segment vasculature, with its performance compared to traditional manual segmentation by surgeons and actual graft weight.
Results: The DL model achieved high concordance with manual methods, exhibiting Dice Similarity Coefficients of 0.94±0.01 for the right lobe and 0.91±0.02 for the left lobe. The liver volume estimates by DL model closely matched those of surgeons, with a mean discrepancy of 9.18 mL, and correlated more strongly with actual graft weights (R-squared value of 0.76 compared to 0.68 for surgeons).
Conclusion: The DL model demonstrates potential as a reliable tool for enhancing preoperative planning in liver transplantation, offering consistency and efficiency in volumetric assessment. Further validation is required to establish its generalizability across various clinical settings and imaging protocols.
논문정보
- Research article
- 2024년 06월 (BRIC 등록일 2024-06-18)
- 국내 연구진
- 의약학 > 종양의학
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