International journal of surgery (London, England) · 2024
Comprehensive deep learning-based assessment of living liver donor CT angiography: from vascular segmentation to volumetric analysis.
СтатьяНазвание и аннотация: Перевод в очереди
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Oh Namkee, Kim Jae-Hun, Rhu Jinsoo, Jeong Woo Kyoung, Choi Gyu-Seong, Kim Jongman, Joh Jae-Won
Аннотация
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.
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.