Going deeper with brain morphometry using neural networks

• URL: http://arxiv.org/abs/2009.03303v1
• Date: Mon, 7 Sep 2020 07:57:13 GMT
• Title: Going deeper with brain morphometry using neural networks
• Authors: Rodrigo Santa Cruz, L\'eo Lebrat, Pierrick Bourgeat, Vincent Dor\'e, Jason Dowling, Jurgen Fripp, Clinton Fookes, Olivier Salvado
• Abstract summary: Deep convolutional neural networks can infer morphometric measurements within a few seconds. We propose a more accurate and efficient neural network model for brain morphometry named HerstonNet.
• Score: 18.851541271793085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convolutional neural networks can infer morphometric measurements within a few seconds. Nevertheless, the accuracy of the devised model for insightful bio-markers (mean curvature and thickness) remains unsatisfactory. In this paper, we propose a more accurate and efficient neural network model for brain morphometry named HerstonNet. More specifically, we develop a 3D ResNet-based neural network to learn rich features directly from MRI, design a multi-scale regression scheme by predicting morphometric measures at feature maps of different resolutions, and leverage a robust optimization method to avoid poor quality minima and reduce the prediction variance. As a result, HerstonNet improves the existing approach by 24.30% in terms of intraclass correlation coefficient (agreement measure) to FreeSurfer silver-standards while maintaining a competitive run-time.

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