Interactive Ultrasound Prostate Cancer Segmentation using Deep Learning with Principal Curve-based Fine-tuning

Segmentation of prostate cancer on ultrasound (US) images is challenging because of the low-intensity contrast around the region of interest (ROI) contour caused by the intestinal gas. To the best of our knowledge, our work is one of the extremely rare groups to segment prostate cancer on US images, which designs a coarse-to-refinement segmentation framework with four merits: 1) it fuses the advantages of the deep learning- and principal curve-based algorithms to locate the ROI and fit the data center automatically, respectively; 2) a principal curve-based polygon searching method is designed by newly adding some constraints so that the performance of our method could be enhanced; 3) the quantum characteristics are added into evolution network, while newly adding numerous- operator scheme and global optimum search schemes; 4) an interpretable mathematical mapping model is developed to denote the ROI contour. Segmentation results show that our network outperforms other medical image segmentation models.