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Precise and fast prediction methods for ischemic areas comprised of deadtissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS)patients are of significant clinical interest. They play an essential role inimproving diagnosis and treatment planning. Computed Tomography (CT) scan isone of the primary modalities for early assessment in patients with suspectedAIS. CT Perfusion (CTP) is often used as a primary assessment to determinestroke location, severity, and volume of ischemic lesions. Current automaticsegmentation methods for CTP mostly use already processed 3D parametric mapsconventionally used for clinical interpretation by radiologists as input.Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+timeinput, where the spatial information over the volume is ignored. In addition,these methods are only interested in segmenting core regions, while predictingpenumbra can be essential for treatment planning. This paper investigatesdifferent methods to utilize the entire 4D CTP as input to fully exploit thespatio-temporal information, leading us to propose a novel 4D convolutionlayer. Our comprehensive experiments on a local dataset of 152 patients dividedinto three groups show that our proposed models generate more precise resultsthan other methods explored. Adopting the proposed 4D mJ-Net, a DiceCoefficient of 0.53 and 0.23 is achieved for segmenting penumbra and coreareas, respectively. The code is available onhttps://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git.

CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke