Artificial intelligence-based technology to make a three-dimensional pelvic model for preoperative simulation of rectal cancer surgery using MRI.

Aim: A new technique that allows visualization of whole pelvic organs with high accuracy and usability is needed for preoperative simulation in advanced rectal cancer surgery. In this study, we developed an automated algorithm to create a three-dimensional (3D) model from pelvic MRI using artificial intelligence (AI) technology. Methods: This study included a total of 143 patients who underwent 3D MRI in a preoperative examination for rectal cancer. The training dataset included 133 patients, in which ground truth labels were created for pelvic vessels, nerves, and bone. A 3D variant of U-net was used for the network architecture. Ten patients who underwent lateral lymph node dissection were used as a validation dataset. The correctness of the vascular labelling was assessed for pelvic vessels and the Dice similarity coefficients calculated for pelvic bone. Results: An automatic segmentation algorithm that extracts the artery, vein, nerve, and pelvic bone was developed, automatically producing a 3D image of the entire pelvis. The total time needed for segmentation was 133 seconds. The success rate of the AI-based segmentation was 100% for the common and external iliac vessels, but the rates for the vesical vein (75%), superior gluteal vein (60%), or accessory obturator vein (63%) were suboptimal. Regarding pelvic bone, the average Dice similarity coefficient between manual and automatic segmentation was 0.97 (standard deviation 0.0043). Conclusion: Though there is room to improve the segmentation accuracy, the algorithm developed in this study can be utilized for surgical simulation in the treatment of advanced rectal cancer.