Title

Deep Learning Framework for Epithelium Density Estimation in Prostate Multi-Parametric Magnetic Resonance Imaging

Document Type

Conference Proceeding

Publication Date

4-1-2020

Journal

Proceedings - International Symposium on Biomedical Imaging

Volume

2020-April

DOI

10.1109/ISBI45749.2020.9098475

Keywords

Deep neural network; Histopathology; MRI; Prostate

Abstract

© 2020 IEEE. Multi-parametric magnetic resonance imaging (mpMRI) permits non-invasive visualization and localization of clinically important cancers in the prostate. However, it cannot fully describe tumor heterogeneity and microstructures that are crucial for cancer management and treatment. Herein, we develop a deep learning framework that could predict epithelium density of the prostate in mpMRI. A deep convolutional neural network is built to estimate epithelium density per voxel-basis. Equipped with an advanced design of the neural network and loss function, the proposed method obtained a SSIM of 0.744 and a MAE of 6.448% in a cross-validation. It also outperformed the competing network. The results are promising as a potential tool to analyze tissue characteristics of the prostate in mpMRI.

This document is currently not available here.

Share

COinS