SPD matrices representing artery anatomy for first-pass effect prediction by aggregated networks with multi-scale attentions

Abstract

ABSTRACT First-pass Effect(FPE) for Endovascular Therapy(EVT) is associated with good clinical outcome (mRS2) of patients with Acute Ischaemic Stroke (AIS). A rapid and accurate prediction of FPE before EVT can help neurointerventionists plan the procedure and avoid delays in restoration of cerebral blood flow. However, there are rare studies focused on FPE prediction on arterial vessel anatomy immediately. The intractable difficulty lies in extracting discriminative features to represent a wide variety of vessels with irregular vessel shapes. In this paper, we propose a Symmetric Positive Definite(SPD) matrix-based feature representation extracted from the centerline of arterial vessels, encoding the global discriminative information over the artery for predicting FPE. Subsequently, a collaborative network of multi-scale Convolutional Neural Network(CNN) and Multiple Layer Perception (MLP) with specific attentions is developed. Specifically, the CNN is used for capturing the features among the multi-scale local neighbours of the curve. MLPs are used for capturing more prominent global discriminative features at different scales. The attention mechanism is used to better filter and extract the useful information for feature fusion. Quantitative experimental results demonstrate that our proposed method is able to predict FPE accurately, outperforming the manually defined features and traditional machine learning-based methods in this task, regarding the metrics of AUC, precision, sensitivity, specificity and accuracy.