Combining unsupervised and supervised learning for predicting the final stroke lesion.

Pinto, Adriano; Pereira, Sérgio; Meier, Raphael; Wiest, Roland; Alves, Victor; Reyes, Mauricio; Silva, Carlos A (2021). Combining unsupervised and supervised learning for predicting the final stroke lesion. Medical image analysis, 69(101888), p. 101888. Elsevier 10.1016/j.media.2020.101888

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Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selection is influenced by clinical diagnosis, which requires delineating the stroke lesion, as well as characterising cerebral blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting the final stroke lesion is an intricate task, due to the variability in lesion size, shape, location and the underlying cerebral haemodynamic processes that occur after the ischaemic stroke takes place. Moreover, since elapsed time between stroke and treatment is related to the loss of brain tissue, assessing and predicting the final stroke lesion needs to be performed in a short period of time, which makes the task even more complex. Therefore, there is a need for automatic methods that predict the final stroke lesion and support physicians in the treatment decision process. We propose a fully automatic deep learning method based on unsupervised and supervised learning to predict the final stroke lesion after 90 days. Our aim is to predict the final stroke lesion location and extent, taking into account the underlying cerebral blood flow dynamics that can influence the prediction. To achieve this, we propose a two-branch Restricted Boltzmann Machine, which provides specialized data-driven features from different sets of standard parametric Magnetic Resonance Imaging maps. These data-driven feature maps are then combined with the parametric Magnetic Resonance Imaging maps, and fed to a Convolutional and Recurrent Neural Network architecture. We evaluated our proposal on the publicly available ISLES 2017 testing dataset, reaching a Dice score of 0.38, Hausdorff Distance of 29.21 mm, and Average Symmetric Surface Distance of 5.52 mm.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

UniBE Contributor:

Meier, Raphael; Wiest, Roland and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1361-8415

Publisher:

Elsevier

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

08 Feb 2021 16:20

Last Modified:

05 Mar 2021 01:35

Publisher DOI:

10.1016/j.media.2020.101888

PubMed ID:

33387909

Uncontrolled Keywords:

Deep learning Image prediction Magnetic resonance imaging Stroke

BORIS DOI:

10.48350/151639

URI:

https://boris.unibe.ch/id/eprint/151639

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