Abstract
In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.
Highlights
Stroke ranks second as leading cause of death worldwide [1], with ischemic stroke being the most common type [2]
Afterwards, we present the results obtained in ISLES 2017 testing dataset, performing a comparison against state-of-the-art methods
We show the importance of having non-imaging clinical information in a neural network, to characterize principal and collateral blood flow hemodynamic and obtain better prediction outcomes
Summary
Stroke ranks second as leading cause of death worldwide [1], with ischemic stroke being the most common type [2]. Ischemic stroke arises from an artery occlusion caused by local thrombolysis, hemodynamic factors or embolic causes. The surrounding area suddenly suffers a blood flow reduction, leading the cells to a transient state slightly above cell death. The hypo-perfused area concerns the tissue at risk, known as salvageable tissue, that can Stroke Prediction With Imaging and Clinical Information eventually reach a non-viable point of failure even after flow restoration [3, 4]. Stroke lesion can be characterized by a core tissue, encompassed by brain dead tissue, and a penumbra tissue corresponding to the salvageable tissue. The temporal evolution of a stroke lesion can be characterized into four main phases: hyper-acute (initial event), acute (6 h after event), subacute (from 24 h) and chronic phase (from 2 weeks) [5]
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