Xiaopeng Yang1, Jae Do Yang2, Hong Pil Hwang2, Hee Chul Yu3, Sungwoo Ahn2, Bong-Wan Kim4, Heecheon You1. 1. Department of Industrial Management and Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea. 2. Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea. 3. Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea. Electronic address: hcyu@jbnu.ac.kr. 4. Department of Liver Transplantation and Hepatobiliary Surgery, Ajou University School of Medicine, Suwon, 16499, South Korea.
Abstract
BACKGROUND AND OBJECTIVE: The present study developed an effective surgical planning method consisting of a liver extraction stage, a vessel extraction stage, and a liver segment classification stage based on abdominal computerized tomography (CT) images. METHODS: An automatic seed point identification method, customized level set methods, and an automated thresholding method were applied in this study to extraction of the liver, portal vein (PV), and hepatic vein (HV) from CT images. Then, a semi-automatic method was developed to separate PV and HV. Lastly, a local searching method was proposed for identification of PV branches and the nearest neighbor approximation method was applied to classifying liver segments. RESULTS: Onsite evaluation of liver segmentation provided by the SLIVER07 website showed that the liver segmentation method achieved an average volumetric overlap accuracy of 95.2%. An expert radiologist evaluation of vessel segmentation showed no false positive errors or misconnections between PV and HV in the extracted vessel trees. Clinical evaluation of liver segment classification using 43 CT datasets from two medical centers showed that the proposed method achieved high accuracy in liver graft volumetry (absolute error, AE = 45.2 ± 20.9 ml; percentage of AE, %AE = 6.8% ± 3.2%; percentage of %AE > 10% = 16.3%; percentage of %AE > 20% = none) and the classified segment boundaries agreed with the intraoperative surgical cutting boundaries by visual inspection. CONCLUSIONS: The method in this study is effective in segmentation of liver and vessels and classification of liver segments and can be applied to preoperative liver surgical planning in living donor liver transplantation.
BACKGROUND AND OBJECTIVE: The present study developed an effective surgical planning method consisting of a liver extraction stage, a vessel extraction stage, and a liver segment classification stage based on abdominal computerized tomography (CT) images. METHODS: An automatic seed point identification method, customized level set methods, and an automated thresholding method were applied in this study to extraction of the liver, portal vein (PV), and hepatic vein (HV) from CT images. Then, a semi-automatic method was developed to separate PV and HV. Lastly, a local searching method was proposed for identification of PV branches and the nearest neighbor approximation method was applied to classifying liver segments. RESULTS: Onsite evaluation of liver segmentation provided by the SLIVER07 website showed that the liver segmentation method achieved an average volumetric overlap accuracy of 95.2%. An expert radiologist evaluation of vessel segmentation showed no false positive errors or misconnections between PV and HV in the extracted vessel trees. Clinical evaluation of liver segment classification using 43 CT datasets from two medical centers showed that the proposed method achieved high accuracy in liver graft volumetry (absolute error, AE = 45.2 ± 20.9 ml; percentage of AE, %AE = 6.8% ± 3.2%; percentage of %AE > 10% = 16.3%; percentage of %AE > 20% = none) and the classified segment boundaries agreed with the intraoperative surgical cutting boundaries by visual inspection. CONCLUSIONS: The method in this study is effective in segmentation of liver and vessels and classification of liver segments and can be applied to preoperative liver surgical planning in living donor liver transplantation.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Perry J Pickhardt; Ronald M Summers; Sungwon Lee; Daniel C Elton; Alexander H Yang; Christopher Koh; David E Kleiner; Meghan G Lubner Journal: Radiol Artif Intell Date: 2022-08-24
Authors: Giammauro Berardi; Marco Colasanti; Roberto Luca Meniconi; Stefano Ferretti; Nicola Guglielmo; Germano Mariano; Mirco Burocchi; Alessandra Campanelli; Andrea Scotti; Alessandra Pecoraro; Marco Angrisani; Paolo Ferrari; Andrea Minervini; Camilla Gasparoli; Go Wakabayashi; Giuseppe Maria Ettorre Journal: Diagnostics (Basel) Date: 2021-11-23