Precise and fast prediction methods for ischemic areas comprised of dead
tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS)
patients are of significant clinical interest. They play an essential role in
improving diagnosis and treatment planning. Computed Tomography (CT) scan is
one of the primary modalities for early assessment in patients with suspected
AIS. CT Perfusion (CTP) is often used as a primary assessment to determine
stroke location, severity, and volume of ischemic lesions. Current automatic
segmentation methods for CTP mostly use already processed 3D parametric maps
conventionally used for clinical interpretation by radiologists as input.
Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time
input, where the spatial information over the volume is ignored. In addition,
these methods are only interested in segmenting core regions, while predicting
penumbra can be essential for treatment planning. This paper investigates
different methods to utilize the entire 4D CTP as input to fully exploit the
spatio-temporal information, leading us to propose a novel 4D convolution
layer. Our comprehensive experiments on a local dataset of 152 patients divided
into three groups show that our proposed models generate more precise results
than other methods explored. Adopting the proposed 4D mJ-Net, a Dice
Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core
areas, respectively. The code is available on
https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git