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http://dx.doi.org/10.9718/JBER.2022.43.6.434

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

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)
Publication Information
Journal of Biomedical Engineering Research / v.43, no.6, 2022 , pp. 434-440 More about this Journal
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
Hepatic transplantation; Segmentation; Volumetric liver; Hepatic resection; Computed tomography;
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