3-D Prostate MR and TRUS Images Detection and Segmentation for Puncture Biopsy

Abstract

Prostate cancer is one of the male cancers with high mortality rate in the world. The mainstream diagnostic method is puncture biopsy. Accurate detection and segmentation of the prostate in MR (Magnetic Resonance) and TRUS (Transrectal Ultrasonography) images is an important prerequisite for image-guided puncture biopsy. Manual or semi-automatic detection and segmentation methods and traditional automatic detection and segmentation methods are difficult to achieve real-time and accurate detection and segmentation under the conditions of poor imaging effect or less labeled data. In this paper, a real-time and accurate automatic detection and segmentation algorithm DRC U-Net for 3D MR and TRUS images of prostate is proposed for prostate puncture biopsy. A novel convolution module that integrates residual connections, dense connections, and deep separable convolutions is proposed for multi-scale feature extraction and fusion. At the same time, deep supervision mechanism and a variety of attention mechanisms are integrated in the network to improve training efficiency and ensure segmentation effect. The experimental results on the public MR dataset Promise12 and the private TRUS dataset show that the real-time performance and accuracy of this method are better than the existing 3D medical image detection and segmentation methods, such as 3D U-Net and V-Net, and also have some advantages compared with the most advanced 3D medical image detection and segmentation methods, which proves that the proposed network structure has important clinical significance and practical value for image-guided puncture biopsy.