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

Authors

DOI:

https://doi.org/10.4108/eai.24-2-2022.173546

Keywords:

deep learning, MRI segmentation, prostate cancer, peripheral zone, cropping

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.

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Published

24-02-2022

How to Cite

Zaridis, D. ., Mylona, E. ., Tachos, N. ., Marias, K. ., Tsiknakis, M. ., & Fotiadis, D. I. . (2022). A smart cropping pipeline to improve prostate’s peripheral zone segmentation on MRI using Deep Learning. EAI Endorsed Transactions on Bioengineering and Bioinformatics, 1(4), e3. https://doi.org/10.4108/eai.24-2-2022.173546