Multi-Channel Feature Pyramid Networks for Prostate Segmentation, Based on Transrectal Ultrasound Imaging

Abstract

Accurate segmentation for transrectal ultrasound imaging (TRUS) is often a challenging medical image processing task. The problem of weak boundary between adjacent prostate tissue and non-prostate tissue, and high similarity between artifact area and prostate area has always been the difficulty of TRUS image segmentation. In this paper, we construct a multi-channel feature pyramid network (MFPN) based on deep convolutional neural network-based prostate segmentation method to process multi-scale feature maps. Each level enhances the edge characteristics of the prostate by controlling the scale of the channel. The optimized regression mechanism of the target area was used to accurately locate the prostate. Experimental results showed that the proposed method achieved the key indicator Dice similarity coefficient and average absolute distance of 0.9651 mm and 0.504 mm, which outperformed state-of-the-art approaches.