Ischemic Stroke Lesion Prediction in CT Perfusion Scans Using Multiple Parallel UNets Following by a PixelLevel 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 timeconsuming 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 handcrafted 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 UNets 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 UNets, 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 stateoftheart approaches.

CRUNet: Cascaded UNet with Residual Mapping for Liver Segmentation in CT Images * Abdominal computed tomography (CT) is a common modality to detect liver lesions. Liver segmentation in CT scan is important for diagnosis and analysis of liver lesions. However, the accuracy of existing liver segmentation methods is slightly insufficient. In this paper, we propose a liver segmentation architecture named CRUNet, which is composed of cascade UNet combined with residual mapping. We make use of the MDice loss function for training in CRUNet, and the secondlevel of cascade network is deeper than the firstlevel to extract more detailed image features. Morphological algorithms are utilized as an intermediateprocessing step to improve the segmentation accuracy. In addition, we evaluate our proposed CRUNet on liver segmentation task under the dataset provided by the 2017 ISBI LiTS Challenge. The experimental result demonstrates that our proposed CRUNet can outperform the stateoftheart methods in term of the performance measures, such as Dice score, VOE, and so on.