Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network

Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

“…Thirteen studies adopted conventional ML algorithms including k-nearest neighbor classification (24), general linear regression (47), random forest (13,15,25,34,36,38,41,48) and gradient boosting (11,26,36) classifiers. Twentyfive studies proposed DL-based approaches consisting of artificial neural network (ANN) (31) and various types of convolutional neural network (CNN) with some of the noteworthy popular architectures, including 2D and 3D U-Net (12,16,17,27,28,39,40,43,49,50), residual network (ResNet) (12,29,37,50), recurrent residual U-Net (R2U-Net) (52) and DeepMedic (32). Four studies applied modifications of the common rectified linear unit (ReLU) activation function for non-linear transformation after each convolution operation, including parametric ReLU, noisy ReLU, and leaky ReLU activation (32,33,40,51).…”

“…Eleven studies used CT perfusion source data and parametric maps as model input for core infarct estimation (12,13,29,31,33,42,44,47,50,53,54), including one study generating a synthesized pseudo-DWI map based on CTP parametric maps (42). Five studies used source images and features derived from non-contrast CT (41,52) and CT angiography (15,45,46).…”

“…In clinical practice, CT is more widely available for acute stroke triage, detecting large vessel occlusion, and selecting candidates for revascularization (8,55). Studies developing models training on CT perfusion data appeared late yet achieved comparable or better performance than MR-input models (12,29,31,33,42,44,47,50,52,53). One study employing a random forest classifier using features extracted from multi-phase CT angiography presented less satisfactory performance with a DSC score of 0.22 (15).…”

“…For studies applying conventional ML classifiers, the pooled DSC score was 0.44 (95%CI 0.34-0.54) for models inputting MR data (11,25,38) and 0.37 (95%CI 0.33-0.41) for a single model using CT data as a reference (13). For studies developing DL-based approaches, the pooled DSC was 0.45 (95%CI 0.38-0.53) for models inputting MR data (16,40,43) and 0.63 (95%CI 0.48-0.78) when using CT data (31,50,52,54). Sustained high heterogeneity was observed in the subgroup of DL models combined with CT data (Higgins I 2 93.9%, p < 0.001).…”

“…Eleven studies were included in the meta-analysis (11,13,16,25,31,38,40,43,50,52,54). Methodological quality assessment of the included studies is shown in Table 2.…”