Integrated extractor, generator and segmentor for ischemic stroke lesion segmentation

Abstract

The challenge of Ischemic Stroke Lesion Segmentation 2018 asks for methods that allow the segmentation of stroke lesion based on acute CT perfusion data, and provided a data set of 103 stroke patients and matching expert segmentations. In this paper, a novel deep learning framework with extractor, generator and segmentor for ischemic stroke lesion segmentation has been proposed. Firstly, the extractor is to extract the feature map from processed perfusion weighted imaging (PWI). Secondly, the output of extractor, cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and time of peak of the residue function (Tmax), etc. as the input of the generator to generated the Diffusion weighted imaging (DWI) modality. Finally, the segmentor is to precisely segment the ischemic stroke lesion using the generated data. In order to overcome the over-fitting, the data augmentation (e.g. random rotations, random crop and radial distortion) is used in training phase. Therefore, generalized dice combined with cross entropy were used as loss function to handle unbalanced data. All networks are trained end-to-end from scratch using the 2018 Ischemic Stroke Lesion Challenge dataset which contains training set of 63 patients and testing set of 40 patients. Our method achieves state-of-the-art segmentation accuracy in the testing set.