Deep learning pipeline for brain MRI acquisition type classification

Hossein Mohammadian Foroushani; Pamela LaMontagne; Lauren Wallace; Jenny Gurney; Daniel Marcus

doi:10.1117/12.2581857

20 April 2021 Deep learning pipeline for brain MRI acquisition type classification

Hossein Mohammadian Foroushani, Pamela LaMontagne, Lauren Wallace, Jenny Gurney, Daniel Marcus

Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 116010G (2021) https://doi.org/10.1117/12.2581857
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

We have developed an effective deep learning pipeline to classify brain magnetic resonance imaging scans automatically into 12 subcategories. The classification is performed by a meta classifier which receives level one predictions from Microsoft's Residual Networks (ResNet), Google’s Neural Architecture Search Network (NASNet) and a text-based classifier on DICOM header series description and combine them to get final classification. The overall classifier was trained, validated and tested on 2750 MRI images from multicenter projects. The classifier was packaged using Docker containerization technology and deployed on a local XNAT instance and tested on 3000 independent imaging sessions with 98.5% accuracy.

Conference Presentation

Citation Download Citation

Hossein Mohammadian Foroushani, Pamela LaMontagne, Lauren Wallace, Jenny Gurney, and Daniel Marcus "Deep learning pipeline for brain MRI acquisition type classification", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 116010G (20 April 2021); https://doi.org/10.1117/12.2581857

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