3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning
Namkee Oh, Jae-Hun Kim, Jinsoo Rhu, Woo Kyoung Jeong, Gyu-Seong Choi, Jong Man Kim, Jae-Won Joh- General Medicine
- Surgery
Background:
This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP).
Materials and methods:
Living liver donors who underwent MRCP using the gradient and spin echo technique followed by three-dimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for the deep learning process. Data were divided into training and test sets at a 9:1 ratio. Performance was assessed using the dice similarity coefficient to compare the model’s segmentation with the manually labeled ground truth.
Results:
The study incorporated 250 cases. There was no difference in the baseline characteristics between the train set (
Conclusion:
The developed automated segmentation model for biliary structures, utilizing MRCP data and deep learning techniques, demonstrated robust performance and holds potential for further advancements in automation.