Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)
As one of the leading causes of mortality and disability worldwide, Acute Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly interrupted because of a blocked artery. Within seconds of AIS onset, the brain cells surrounding the blocked artery die, which leads to the progressi...
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Zusammenfassung: | As one of the leading causes of mortality and disability worldwide, Acute
Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly
interrupted because of a blocked artery. Within seconds of AIS onset, the brain
cells surrounding the blocked artery die, which leads to the progression of the
lesion. The automated and precise prediction of the existing lesion plays a
vital role in the AIS treatment planning and prevention of further injuries.
The current standard AIS assessment method, which thresholds the 3D measurement
maps extracted from Computed Tomography Perfusion (CTP) images, is not accurate
enough. Due to this fact, in this article, we propose the imbalanced Temporal
Deep Gaussian Process (iTDGP), a probabilistic model that can improve AIS
lesions prediction by using baseline CTP time series. Our proposed model can
effectively extract temporal information from the CTP time series and map it to
the class labels of the brain's voxels. In addition, by using batch training
and voxel-level analysis iTDGP can learn from a few patients and it is robust
against imbalanced classes. Moreover, our model incorporates a post-processor
capable of improving prediction accuracy using spatial information. Our
comprehensive experiments, on the ISLES 2018 and the University of Alberta
Hospital (UAH) datasets, show that iTDGP performs better than state-of-the-art
AIS lesion predictors, obtaining the (cross-validation) Dice score of 71.42%
and 65.37% with a significant p |
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DOI: | 10.48550/arxiv.2211.09068 |