Encoder-decoder with dense dilated spatial pyramid pooling for prostate MR images segmentation

Abstract

Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseases. It faces enormous challenges because of the low contrast of the tissue boundary and the small effective area of the prostate MR images. In order to solve these problems, we propose a novel end-to-end professional network which consists of an Encoder-Decoder structure with dense dilated spatial pyramid pooling (DDSPP) for prostate segmentation based on deep learning. First, the DDSPP module is used to extract the multi-scale convolution features in the prostate MR images, and then the decoder is used to capture the clear boundary of prostate. Competitive results are produced over state of the art on 130 MR images which key metrics Dice similarity coefficient (DSC) and Hausdorff distance (HD) are 0.954 and 1.752 mm respectively. Experimental results show that our method has high accuracy and robustness.

Keywords:

• Prostate
• MRI
• Encoder-Decoder
• DDSPP

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by National Nature Science Foundation of China (NSFC) under grant No. 61771340; Tianjin Science and Technology Major Projects and Engineering under grant No. 17ZXSCSY00060, 17ZXSCSY00090; Tianjin Natural Science Foundation under grant No.17JCQNJC01400 and the Program for Innovative Research Team in University of Tianjin under grant No.TD13-5034.