Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation Learning

Abstract

Computed Tomography Perfusion (CTP) images have drawn extensive attention in acute ischemic stroke assessment due to its imaging speed and ability to provide dynamic perfusion quantification. However, the cerebral ischemic infarcted core has high individual variability and low contrast, and multiple CTP parametric maps need to be referred for precise delineation of the core region. It has thus become a challenging task to develop automatic segmentation algorithms. The widely applied segmentation algorithms such as U-Net lack specific modeling for image subtype in the dataset, and thus the performance remains unsatisfactory. In this paper, we propose a novel cluster-representation learning approach to address these difficulties. Specifically, we first cluster the training samples based on their similarities of the segmentation difficulty. Each cluster represents a different subtype of training images and is then used to train its own cluster-representative model. The models will be capable of extracting cluster-representative features from training samples as clustering priors, which are further fused into an overall segmentation model (for all training samples). The fusion mechanism is able to adaptively select optimal subset(s) of clustering priors which can further guide the segmentation of each unseen testing image and reduce influences from high variability of CTP images. We have applied our method on 94 subjects of ISLES 2018 dataset. By comparing with the baseline U-Net, the experiments have shown an absolute increase of 8% in Dice score and a reduction of 10mm in Hausdorff Distance for ischemic infarcted core segmentation. This method can also be generalized to other U-Net-like architectures to further improve their representative capacity.

L. Zhang and D. Qian—Equally Contributed.