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Fully automatic bile duct segmentation in magnetic resonance cholangiopancreatography for biliary surgery planning
Haisu Tao1, Junfeng Wang2, Kangwei Guo2
1Department of Hepatobiliary Surgery Ⅰ, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou 510280, China; Liver Cancer Center of Zhujiang Hospital, Guangzhou 510280, China; South China Institute of National Engineering Research Center of Innovation and Application of Minimally Invasive Instruments, Guangzhou 510280, China; Special Medical Service Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510282 China.
Objectives:
To automatically and accurately perform three-dimensional reconstruction of dilated and non-dilated bile ducts based on magnetic resonance cholangiopancreatography (MRCP) data, assisting in the formulation of optimal surgical plans and guiding precise bile duct surgery.
Methods:
A total of 249 consecutive patients who underwent standardized 3D-MRCP scans were randomly divided into a training cohort (n = 208) and a testing cohort (n = 41). Ground truth segmentation was manually delineated by two hepatobiliary surgeons or radiologists following industry certification procedures and reviewed by two expert-level physicians for biliary surgery planning. The deep learning semantic segmentation model was constructed using the nnU-Net framework. Model performance was assessed by comparing model predictions with ground truth segmentation as well as real surgical scenarios. The generalization of the model was tested on a dataset of 10 3D-MRCP scans from other centers, with ground truth segmentation of biliary structures.
Results:
The evaluation was performed on 41 internal test sets and 10 external test sets, with mean Dice Similarity Coefficient (DSC) values of respectively 0.9403 and 0.9070. The correlation coefficient between the 3D model based on automatic segmentation predictions and the ground truth results exceeded 0.95. The 95 % limits of agreement (LoA) for biliary tract length ranged from -4.456 to 4.781, and for biliary tract volume ranged from -3.404 to 3.650 ml. Furthermore, the intraoperative Indocyanine green (ICG) fluorescence imaging and operation situation validated that this model can accurately reconstruct biliary landmarks.
Conclusion:
By leveraging a deep learning algorithmic framework, an AI model can be trained to perform automatic and accurate 3D reconstructions of non-dilated bile ducts, thereby providing guidance for the preoperative planning of complex biliary surgeries.
