A smart cropping pipeline to improve prostate’s peripheral zone segmentation on MRI using Deep Learning

Abstract

INTRODUCTION: Although accurate segmentation of the prostatic subregions is a crucial step for prostate cancer diagnosis, it remains a challenge.

OBJECTIVES: To propose a deep learning (DL)-based cropping pipeline to improve the performance of DL networks for segmenting the prostate’s peripheral zone.

METHODS: A U-net network was trained to crop the area around the peripheral zone on MRI in order to reduce the class imbalance between foreground and background pixels. The DL-cropping was compared with the standard center-cropping using three segmentation networks.

RESULTS: The DL-cropping improved significantly the segmentation performance in terms of Dice score, Sensitivity, Hausdorff Distance, and Average Surface Distance, for all three networks. The improvement in Dice Score was 34%, 13% and 16% for the U-net, Dense U-net and Bridged U-net, respectively. CONCLUSION: For all the evaluated networks, the proposed DL-cropping technique outperformed the standard center-cropping.