Meeting Banner
Abstract #4120

Non-invasive Gleason Score Classification with VERDICT-MRI 

Vanya V Valindria1, Saurabh Singh2, Eleni Chiou1, Thomy Mertzanidou1, Baris Kanber1, Shonit Punwani2, Marco Palombo1, and Eleftheria Panagiotaki1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom

This study proposes non-invasive Gleason Score (GS) classification for prostate cancer with VERDICT-MRI using convolutional neural networks (CNNs). We evaluate GS classification using parametric maps from the VERDICT prostate model with compensated relaxation. We classify lesions using two CNN architectures: DenseNet and SE-ResNet. Results show that VERDICT achieves high GS classification performance using SE-ResNet with all parametric maps as input. Also in comparison with published GS classification multi-parametric MRI studies, VERDICT maps achieve higher metrics.

This abstract and the presentation materials are available to members only; a login is required.

Join Here