Practical applications of machine learning in imaging trials

Jacob Y. Hesterman; Elliot Greenblatt; Andrew Novicki; Ali Ghayoor; Tyler Wellman; Brian Avants

doi:10.1117/12.2581697

5 March 2021 Practical applications of machine learning in imaging trials

Jacob Y. Hesterman, Elliot Greenblatt, Andrew Novicki, Ali Ghayoor, Tyler Wellman, Brian Avants

Proceedings Volume 11624, Visualizing and Quantifying Drug Distribution in Tissue V; 116240I (2021) https://doi.org/10.1117/12.2581697
Event: SPIE BiOS, 2021, Online Only

Abstract

Machine learning and deep learning are ubiquitous across a wide variety of scientific disciplines, including medical imaging. An overview of multiple application areas along the imaging chain where deep learning methods are utilized in discovery and clinical quantitative imaging trials is presented. Example application areas along the imaging chain include quality control, preprocessing, segmentation, and scoring. Within each area, one or more specific applications is demonstrated, such as automated structural brain MRI quality control assessment in a core lab environment, super-resolution MRI preprocessing for neurodegenerative disease quantification in translational clinical trials, and multimodal PET/CT tumor segmentation in prostate cancer trials. The quantitative output of these algorithms is described, including their impact on decision making and relationship to traditional read-based methods. Development and deployment of these techniques for use in quantitative imaging trials presents unique challenges. The interplay between technical and scientific domain knowledge required for algorithm development is highlighted. The infrastructure surrounding algorithm deployment is critical, given regulatory, method robustness, computational, and performance considerations. The sensitivity of a given technique to these considerations and thus complexity of deployment is task- and phase-dependent. Context is provided for the infrastructure surrounding these methods, including common strategies for data flow, storage, access, and dissemination as well as application-specific considerations for individual use cases.

Conference Presentation

Citation Download Citation

Jacob Y. Hesterman, Elliot Greenblatt, Andrew Novicki, Ali Ghayoor, Tyler Wellman, and Brian Avants "Practical applications of machine learning in imaging trials", Proc. SPIE 11624, Visualizing and Quantifying Drug Distribution in Tissue V, 116240I (5 March 2021); https://doi.org/10.1117/12.2581697

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