Abstract

Cerebrovascular diseases, in particular ischemic stroke, are one of the leading global causes of death in developed countries. Perfusion CT and/or MRI are ideal imaging modalities for characterizing affected ischemic tissue in the hyper-acute phase. If infarct growth over time could be predicted accurately from functional acute imaging protocols together with advanced machine-learning based image analysis, the expected benefits of treatment options could be better weighted against potential risks. The quality of the outcome prediction by convolutional neural networks (CNNs) is so far limited, which indicates that even highly complex deep learning algorithms are not fully capable of directly learning physiological principles of tissue salvation through weak supervision due to a lack of data (e.g., follow-up segmentation). In this work, we address these current shortcomings by explicitly taking into account clinical expert knowledge in the form of segmentations of the core and its surrounding penumbra in acute CT perfusion images (CTP), that are trained to be represented in a low-dimensional non-linear shape space. Employing a multi-scale CNN (U-Net) together with a convolutional auto-encoder, we predict lesion tissue probabilities for new patients. The predictions are physiologically constrained to a shape embedding that encodes a continuous progression between the core and penumbra extents. The comparison to a simple interpolation in the original voxel space and an unconstrained CNN shows that the use of such a shape space can be advantageous to predict time-dependent growth of stroke lesions on acute perfusion data, yielding a Dice score overlap of 0.46 for predictions from expert segmentations of core and penumbra. Our interpolation method models monotone infarct growth robustly on a linear time scale to automatically predict clinically plausible tissue outcomes that may serve as a basis for more clinical measures such as the expected lesion volume increase and can support the decision making on treatment options and triage.

Highlights

  • Cerebrovascular diseases, in particular strokes, are one of the leading global causes of death in developed countries (1)

  • In this work we have shown the feasibility of using interpolations between low-dimensional shape embeddings of core and penumbra segmentations for improving the prediction of stroke lesion tissue outcome

  • We could show that a convolutional auto-encoder (CAE) is able to model the main variances of volumetric stroke shapes resulting in good reconstructions on test data

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Summary

Introduction

Cerebrovascular diseases, in particular strokes, are one of the leading global causes of death in developed countries (1). Acute stroke, which is usually caused by the blockage of cerebral blood flow due to a blood clot, is often diagnosed through CT or MR perfusion imaging (beside others, such as CTA). In contrast to native CT or standard MR sequences, such as T2 or FLAIR, perfusion images with their apparent functional signals enable the delineation of the potential infarct area even in the early acute phase and allow to visually assess the expected stroke severity, which helps the radiologist to come to a final therapy decision as early as possible. In order to decide for a treatment the doctor has to weigh the risk of a therapy such as thrombolysis or thrombectomy against the expected outcome. As evident by a decrease in CBV, severe tissue injury and permanent vascular collapse have occurred

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