Going deeper with brain morphometry using neural networks

Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat, Vincent Dore, Jason Dowling, Jurgen Fripp, Clinton Fookes, Olivier Salvado

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

5 Citations (Scopus)


Brain morphometry from magnetic resonance imaging (MRI) is commonly used for estimating imaging biomarkers for many neurodegenerative diseases, including Alzheimer's. Recent work showed that deep convolutional neural networks could estimate morphometric measurements directly from 3D brain MRI within a few seconds, but with limited accuracy, especially for mean curvature and thickness. In this paper, we propose a more accurate and efficient neural network model for brain morphometry named HerstonNet: we developed a 3D ResNet-based neural network to learn rich features directly from MRI, designed a multi-scale regression scheme by predicting morphometric measures at different resolutions, and applied a robust optimization method to avoid poor quality minima, resulting in lower prediction error variance. HerstonNet outperforms the existing approach by 24.30% in terms of intraclass correlation coefficient (agreement measure) to FreeSurfer silver-standard while maintaining a competitive run-time.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
EditorsLudivine Fluneau
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781665412469
ISBN (Print)9781665429474
Publication statusPublished - 13 Apr 2021
Externally publishedYes
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021


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