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Measurements of the Hepatectomy Rate and Regeneration Rate Using Deep Learning in CT Scan of Living Donors

딥러닝을 이용한 CT 영상에서 생체 공여자의 간 절제율 및 재생률 측정

  • Sae Byeol, Mun (Medical Devices R&D Center, Gachon University Gil Medical Center) ;
  • Young Jae, Kim (Department of Biomedical Engineering, College of IT Convergence, Gachon University) ;
  • Won-Suk, Lee (Department of Surgery, Gil Medical Center, Gachon University College of Medicine) ;
  • Kwang Gi, Kim (Medical Devices R&D Center, Gachon University Gil Medical Center)
  • 문새별 (가천대 길병원 의료기기 R&D 센터) ;
  • 김영재 (가천대학교 IT융합대학 의공학과) ;
  • 이원석 (가천대학교 의과대학 길병원 외과) ;
  • 김광기 (가천대 길병원 의료기기 R&D 센터)
  • Received : 2022.10.19
  • Accepted : 2022.12.19
  • Published : 2022.12.31

Abstract

Liver transplantation is a critical used treatment method for patients with end-stage liver disease. The number of cases of living donor liver transplantation is increasing due to the imbalance in needs and supplies for brain-dead organ donation. As a result, the importance of the accuracy of the donor's suitability evaluation is also increasing rapidly. To measure the donor's liver volume accurately is the most important, that is absolutely necessary for the recipient's postoperative progress and the donor's safety. Therefore, we propose liver segmentation in abdominal CT images from pre-operation, POD 7, and POD 63 with a two-dimensional U-Net. In addition, we introduce an algorithm to measure the volume of the segmented liver and measure the hepatectomy rate and regeneration rate of pre-operation, POD 7, and POD 63. The performance for the learning model shows the best results in the images from pre-operation. Each dataset from pre-operation, POD 7, and POD 63 has the DSC of 94.55 ± 9.24%, 88.40 ± 18.01%, and 90.64 ± 14.35%. The mean of the measured liver volumes by trained model are 1423.44 ± 270.17 ml in pre-operation, 842.99 ± 190.95 ml in POD 7, and 1048.32 ± 201.02 ml in POD 63. The donor's hepatectomy rate is an average of 39.68 ± 13.06%, and the regeneration rate in POD 63 is an average of 14.78 ± 14.07%.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터육성지원사업(IITP-2021-2017-0-01630)과 경기도의 경기도지역협력연구센터 사업(No.GRRC-Gachon2020(B01)), 2021년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행하였음(No. NRF-2021R1A5A2030333).

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