V-net and u-net for ischemic stroke lesion segmentation in a small dataset of perfusion data

Abstract

Ischemic stroke is the result of an obstruction within a brain blood vessel, blocking the fresh blood flow, resulting in a tissue lesion. Early prediction of the ischemic stroke lesion region is important because it can help to choose the most suitable treatment. However, that is not trivial since current medical data, such as CT and MRI, have no explicit information about the future extension of the permanent lesion. A step towards efficiently using these data to predict the lesions is the use of Deep Convolutional Neural Networks as they are able to extract “hidden” information from the data when a reasonable labeled dataset is available and the deep networks are used properly. In order to try to extract this information, we have tested two different deep network architectures that are the state of the art in segmentation problems: V-net and U-net. In both networks, we tried different configurations, such as depth variations, pixel interpolations, MRI image combinations, among others. Experiments showed the following: normalizing the voxels sizes results in better training and predictions; deeper U-Net performs slightly better than the shallower U-Net, however it requires much more computation for only a small gain in accuracy; the inclusion of CT modality improved slightly the results; the use of only perfusion maps brought much better results than the use of raw perfusion data; smaller lesions are harder to detect properly.