Simulation based evaluation framework for deep learning unsupervised anomaly detection on brain FDG-PET

Ravi Hassanaly; Simona Bottani; Benoit Sauty; Olivier Colliot; Ninon Burgos

doi:10.1117/12.2653893

3 April 2023 Simulation based evaluation framework for deep learning unsupervised anomaly detection on brain FDG-PET

Ravi Hassanaly, Simona Bottani, Benoit Sauty, Olivier Colliot, Ninon Burgos

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246428 (2023) https://doi.org/10.1117/12.2653893
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Unsupervised anomaly detection using deep learning models is a popular computer-aided diagnosis approach because it does not need annotated data and is not restricted to the diagnosis of a disease seen during training. Such approach consists in first learning the distribution of anomaly free images. Images presenting anomalies are then detected as outliers of this distribution. These approaches have been widely applied in neuroimaging to detect sharp and localized anomalies such as tumors or white matter hyper-intensities from structural MRI. In this work, we aim to detect anomalies from FDG PET images of patients with Alzheimer’s disease. In this context, the anomalies can be subtle and difficult to delineate, making the task more difficult and meaning that no ground truth exists to evaluate the approaches. We thus propose a framework to evaluate unsupervised anomaly detection approaches that consists in simulating realistic anomalies from images of healthy subjects. We demonstrate the use of this framework by evaluating an approach based on a 3D variational autoencoder.

Citation Download Citation

Ravi Hassanaly, Simona Bottani, Benoit Sauty, Olivier Colliot, and Ninon Burgos "Simulation based evaluation framework for deep learning unsupervised anomaly detection on brain FDG-PET", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246428 (3 April 2023); https://doi.org/10.1117/12.2653893

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