Automatic segmentation of male pelvic floor soft tissue structures for anatomical simulation and morphological assessment in lower rectal cancer surgery.

Background: Pelvic anatomy is a complex network of organs that varies between individuals. Understanding the anatomy of individual patients is crucial for precise rectal cancer surgeries. Therefore, developing technology that can allow visualization of anatomy before surgery is necessary. This study aims to develop an auto-segmentation model of pelvic structures using AI technology and to evaluate the accuracy of the model toward preoperative anatomical understanding.

Methods: Data were collected from 63 male patients who underwent 3D MRI during a preoperative examination for colorectal and urogenital diseases between November 2015 and July 2019 and from 11 healthy male volunteers. Eleven organs and tissues were segmented. The model was developed using a threefold cross-validation process with a total of 59 cases as development data. The accuracy was evaluated with the separately prepared test data using dice similarity coefficient (DSC), true positive rate (TPR), and positive predictive value (PPV) by comparing AI-segmented data with manual-segmented data.

Results: The highest value of DSC, TPR, and PPV were 0.927, 0.909, and 0.948 for the internal anal sphincter (including the rectum), respectively. On the other hand, the lowest values were 0.384, 0.772, and 0.263 for the superficial transverse perineal muscle, respectively. While there were differences among organs, the overall quality of automatic segmentation was maintained in our model, suggesting that the morphological characteristics of the organs may influence the accuracy.

Conclusions: We developed an auto-segmentation model that can independently delineate soft-tissue structures in the male pelvis using 3D T2-weighted MRIs, providing valuable assistance to doctors in understanding pelvic anatomy.