End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT

Abstract

(1) Background: CT perfusion (CTP) is used to quantify cerebral hypoperfusion in acute ischemic stroke. Conventional attenuation curve analysis is not standardized and might require input from expert users, hampering clinical application. This study aims to bypass conventional tracer-kinetic analysis with an end-to-end deep learning model to directly categorize patients by stroke core volume from raw, slice-reduced CTP data. (2) Methods: In this retrospective analysis, we included patients with acute ischemic stroke due to proximal occlusion of the anterior circulation who underwent CTP imaging. A novel convolutional neural network was implemented to extract spatial and temporal features from time-resolved imaging data. In a classification task, the network categorized patients into small or large core. In ten-fold cross-validation, the network was repeatedly trained, evaluated, and tested, using the area under the receiver operating characteristic curve (ROC-AUC). A final model was created in an ensemble approach and independently validated on an external dataset. (3) Results: 217 patients were included in the training cohort and 23 patients in the independent test cohort. Median core volume was 32.4 mL and was used as threshold value for the binary classification task. Model performance yielded a mean (SD) ROC-AUC of 0.72 (0.10) for the test folds. External independent validation resulted in an ensembled mean ROC-AUC of 0.61. (4) Conclusions: In this proof-of-concept study, the proposed end-to-end deep learning approach bypasses conventional perfusion analysis and allows to predict dichotomized infarction core volume solely from slice-reduced CTP images without underlying tracer kinetic assumptions. Further studies can easily extend to additional clinically relevant endpoints.

Keywords: CT perfusion; contrast-enhanced perfusion imaging; convolutional neural networks; deep learning; end-to-end modeling; stroke.

Figure 1

Flowchart of patient selection for the training and independent test cohort. CTP = CT perfusion.

Figure 2

Model architecture overview and detailed, zoomed-in view of the spatial and temporal feature extraction process. The selected slices A and B are fed into identical submodels for spatial and temporal feature extraction. Spatial feature extraction consists of identical, pretrained VGG19 networks for each timepoint of the input images. The resulting feature vector is passed on to the temporal feature extraction. 1D convolutions with two different kernel sizes are carried out in a global and local pathway. The extracted features A and B for both submodels are concatenated, fully connected (FC), and classified.

Figure 3

ROC curves for test data in the 10-fold CV. Mean (SD) ROC-AUC for the CV test folds was 0.72 (0.10). CV = cross-validation, ROC-AUC = area under the receiver operator characteristics curve.