Automatic Prostate Segmentation on MR Images Using Enhanced Holistically-Nested Networks
Dong Jit, Jinzhao Qian, Jun Yu, Toru Kurihara, Shu Zhan · 2018
Magnetic resonance(MR) imaging has shown to be succeed in detecting and visualizing the prostate location. The accurate segmentation of the prostate gland from MR images is necessary for clinical applications. However, the segmentation of prostate is also a challenging task because of the shape of prostate varies significantly and the inhomogeneous intensity distributions in different scans. In this paper, we present an automatic deep learning method for prostate MR images segmentation using the enhanced holistically-nested framework. The network Holistically-Nested Networks(HNN) was first proposed as an image-to-image solution to extract object edges and boundaries visually. We modify HNN via putting additional skip connections from later stages to early stages in order to combine both low-level features and high-level features. The deeper framework exploits multi-level and multi-scale information for the image-to-image prediction in a holistic manner. Experimental evaluation demonstrates that significant segmentation accuracy has been achieved by our proposed enhanced holistically-nested networks compared to other deep learning approaches.