Purpose: To assess
acute ischemic stroke (AIS) severity,
infarct is segmented using computed tomography perfusion (
CTP) software, such as RAPID, Sphere, and Vitrea, relying on contralateral hemisphere thresholds. Since this approach is potentially patient dependent, we investigated whether convolutional neural networks (CNNs) could achieve better performances without the need for contralateral hemisphere thresholds. Approach:
CTP and diffusion-weighted imaging (DWI) data were retrospectively collected for 63 AIS patients. Cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak, mean-transit-time (MTT), and delay time maps were generated using Vitrea
CTP software. U-net shaped CNNs were developed, trained, and tested for 26 different input
CTP parameter combinations.
Infarct labels were segmented from DWI volumes registered with
CTP volumes.
Infarct volumes were reconstructed from two-dimensional
CTP infarct segmentations. To remove erroneous segmentations, conditional random field (CRF) postprocessing was applied and compared with prior results. Spatial and volumetric
infarct agreement was assessed between DWI and
CTP (CNNs and commercial software) using median
infarct difference, median absolute error, dice coefficient, positive predictive value. Results: The most accurate combination of parameters for CNN segmenting
infarct using CRF postprocessing was CBF, CBV, and MTT (4.83 mL, 10.14 mL, 0.66, 0.73). Commercial software results are: RAPID = (2.25 mL, 21.48 mL, 0.63, 0.70), Sphere = (7.57 mL, 17.74 mL, 0.64, 0.70), Vitrea = (6.79 mL, 15.28 mL, 0.63, 0.72). Conclusions: Use of CNNs with multiple input perfusion parameters has shown to be accurate in segmenting
infarcts and has the ability to improve clinical workflow by eliminating the need for contralateral hemisphere comparisons.