Stroke Lesion Segmentation with 2D Novel CNN Pipeline and Novel Loss Function

Abstract

Recently, CT perfusion (CTP) has been used to triage ischemic stroke patients in the early stage, because of its speed, availability, and lack of contraindications. But CTP data alone, even with the generated perfusion maps is not enough to describe the precise location of infarct core or penumbra. Considering the good performance demonstrated on Diffusion Weighted Imaging (DWI), We propose a CTP data analysis technique using Generative Adversarial Networks (GAN) [2] to generate DWI, and segment the regions of ischemic stroke lesion on top of the generated DWI based on convolutional neutral network (CNN) and a novel loss function. Specifically, our CNN structure consists of a generator, a discriminator and a segmentator. The generator synthesizes DWI from CT, which generates a high quality representation for subsequent segmentator. Meanwhile the discriminator competes with the generator to identify whether its input DWI is real or generated. And we propose a novel segmentation loss function that contains a weighted cross-entropy loss and generalized dice loss [1] to balance the positive and negative loss in the training phase. And the weighted cross entropy loss can highlight the area of stroke lesion to enhance the structural contrast. We also introduce other techniques in our network, like GN [13], Maxout [14] in the proposed network. Data augmentation is also used in the training phase. In our experiments, an average dice coefficient of 60.65% is achieved with four-fold cross-validation. From the results of our experiments, this novel network combined with the proposed loss function achieved a better performance in CTP data analysis than traditional methods using CTP perfusion parameters.