Aim Although MRI has a substantial role in directing treatment decisions for locally advanced rectal cancer, precise interpretation of the findings is not necessarily available at every institution. In this study, we aimed to develop artificial intelligence-based software for the segmentation of rectal cancer that can be used for staging to optimize treatment strategy and for preoperative surgical simulation. Method Images from a total of 201 patients who underwent preoperative MRI were analyzed for training data. The resected specimen was processed in a circular shape in 103 cases. Using these datasets, ground-truth labels were prepared by annotating MR images with ground-truth segmentation labels of tumor area based on pathologically confirmed lesions. In addition, the areas of rectum and mesorectum were also labeled. An automatic segmentation algorithm was developed using a U-net deep neural network. Results The developed algorithm could estimate the area of the tumor, rectum, and mesorectum. The Dice similarity coefficients between manual and automatic segmentation were 0.727, 0.930, and 0.917 for tumor, rectum, and mesorectum, respectively. The T2/T3 diagnostic sensitivity, specificity, and overall accuracy were 0.773, 0.768, and 0.771, respectively. Conclusion This algorithm can provide objective analysis of MR images at any institution, and aid risk stratification in rectal cancer and the tailoring of individual treatments. Moreover, it can be used for surgical simulations.

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 threedimensional (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.