Deep Labeling of fMRI Brain Networks Using Cloud Based Processing

• URL: http://arxiv.org/abs/2209.08200v1
• Date: Fri, 16 Sep 2022 23:59:42 GMT
• Title: Deep Labeling of fMRI Brain Networks Using Cloud Based Processing
• Authors: Sejal Ghate, Alberto Santa-Maria Pang, Ivan Tarapov, Haris I Sair, Craig K Jones
• Abstract summary: Resting state fMRI is used in neurosurgical pre-planning to visualize functional regions and assess regional activity. We propose an end-to-end reproducible pipeline which incorporates image processing of rs-fMRI in a cloud-based workflow. We use deep learning to automate the classification of Resting State Networks (RSNs)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resting state fMRI is an imaging modality which reveals brain activity localization through signal changes, in what is known as Resting State Networks (RSNs). This technique is gaining popularity in neurosurgical pre-planning to visualize the functional regions and assess regional activity. Labeling of rs-fMRI networks require subject-matter expertise and is time consuming, creating a need for an automated classification algorithm. While the impact of AI in medical diagnosis has shown great progress; deploying and maintaining these in a clinical setting is an unmet need. We propose an end-to-end reproducible pipeline which incorporates image processing of rs-fMRI in a cloud-based workflow while using deep learning to automate the classification of RSNs. We have architected a reproducible Azure Machine Learning cloud-based medical imaging concept pipeline for fMRI analysis integrating the popular FMRIB Software Library (FSL) toolkit. To demonstrate a clinical application using a large dataset, we compare three neural network architectures for classification of deeper RSNs derived from processed rs-fMRI. The three algorithms are: an MLP, a 2D projection-based CNN, and a fully 3D CNN classification networks. Each of the net-works was trained on the rs-fMRI back-projected independent components giving >98% accuracy for each classification method.

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