Explainability-guided Mathematical Model-Based Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy

Abstract

Accurate segmentation of the prostate is important to image-guided prostate biopsy and brachytherapy treatment planning. However, the incompleteness of prostate boundary increases the challenges in the automatic ultrasound prostate segmentation task. In this work, an automatic coarse-to-fine framework for prostate segmentation was developed and tested. Our framework has four metrics: first, it combines the ability of deep learning model to automatically locate the prostate and integrates the characteristics of principal curve that can automatically fit the data center for refinement. Second, to well balance the accuracy and efficiency of our method, we proposed an intelligent determination of the data radius algorithm-based modified polygon tracking method. Third, we modified the traditional quantum evolution network by adding the numerous-operator scheme and global optimum search scheme for ensuring population diversity and achieving the optimal model parameters. Fourth, we found a suitable mathematical function expressed by the parameters of the machine learning model to smooth the contour of the prostate. Results on the multiple datasets demonstrate that our method has good segmentation performance.