PSP net-based automatic segmentation network model for prostate magnetic resonance imaging

Abstract

Purpose: Prostate cancer is a common cancer. To improve the accuracy of early diagnosis, we propose a prostate Magnetic Resonance Imaging (MRI) segmentation model based on Pyramid Scene Parsing Network (PSP Net).

Method: A total of 270 prostate MRI images were collected, and the data set was divided. Contrast limited adaptive histogram equalization (CLAHE) was enhanced in this study. We use the prostate MRI segmentation model based on PSP net, and use segmentation accuracy, under segmentation rate, over segmentation rate and receiver operating characteristic (ROC) curve evaluation index to compare the segmentation effect based on FCN and U-Net.

Results: PSP net has the highest segmentation accuracy of 0.9865, over segmentation rate of 0.0023, under segmentation rate of 0.1111, which is less than FCN and U-Net. The ROC curve of PSP net is closest to the upper left corner, AUC is 0.9427, larger than FCN and U-Net.

Conclusion: This paper proves through a large number of experimental results that the prostate MRI automatic segmentation network model based on PSP Net is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

Keywords: Convolutional neural network; Image enhancement; Magnetic resonance imaging; PSP Net; Prostate cancer.