Ischemic Stroke Lesion Prediction in CT Perfusion Scans Using Multiple Parallel U-Nets Following by a Pixel-Level Classifier

It is critical to know what brain regions are affected by an ischemic stroke, as this enables doctors to make more effective decisions about stroke patient therapy. These regions are often identified by segmenting computed tomography perfusion (CTP) images. Previously, this task has been done manually by an expert. However, manual segmentation is an extremely tedious and time-consuming process, that is not suitable for ischemic stroke lesion segmentation as it is highly time sensitive. In addition, these approaches require an expert to do the segmentation task, who may not be available and are prone to errors. Several automatic medical image analysis methods have been proposed for ischemic stroke lesion segmentation. These approaches, typically, use hand-crafted features that are predefined to represent the input data. However, because of the irregular and physiologically shapes, ischemic stroke lesions cannot be properly predicted, in an automatic way, using simple predefined features. In this work, we propose an automatic prediction algorithm that learns an effective model for segmenting the ischemic stroke lesion. This learned model first uses four 2D U-Nets to, separately, extract valuable information about the location of the stroke lesion from four CTP maps (CBV, CBF, MTT, Tmax). The model then combines the probability maps extracted by the U-Nets, to decide whether the pixels are either lesion or healthy tissues. This approach uses information about each pixel, as well as its neighborhood, to learn the stroke lesion, despite their varying shapes. The segmentation performance is evaluated using dice similarity coefficient (DSC), volume similarity (VS), and Recall. We have used this new algorithm on ISLES 2018 challenge dataset and found that our approach achieved results that are better than state-of-the-art approaches.