| Abstract: | CT perfusion imaging is commonly used for infarct core quantification in acute ischemic stroke patients. The outcomes and perfusion maps of CT perfusion software, however, show many discrepancies between vendors. We aim to perform infarct core segmentation directly from CT perfusion source data using machine learning, excluding the need to use the perfusion maps from standard CT perfusion software. To this end, we present a symmetry-aware spatio-temporal segmentation model that encodes the micro-perfusion dynamics in the brain, while decoding a static segmentation map for infarct core assessment. Our proposed spatio-temporal PerfU-Net employs an attention module on the skip-connections to match the dimensions of the encoder and decoder. We train and evaluate the method on 94 and 62 scans, respectively, using the Ischemic Stroke Lesion Segmentation (ISLES) 2018 challenge data. We achieve state-of-the-art results compared to methods that only use CT perfusion source imaging with a Dice score of 0.46. We are almost on par with methods that use perfusion maps from third party software, whilst it is known that there is a large variation in these perfusion maps from various vendors. Moreover, we achieve improved performance compared to simple perfusion map analysis, which is used in clinical practice. |
