Detection of Large Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations
This work addresses the need for automated diagnosis in clinical workflows for ischemic stroke patients, though it is incremental as it applies existing deep learning methods with a novel augmentation technique.
The paper tackled automated detection of large vessel occlusions in ischemic stroke using deep learning, achieving an ROC AUC of 0.87 with a 3D-DenseNet model and improving from a baseline of 0.56 to 0.85 with data augmentation on 100 datasets.
Computed Tomography Angiography is a key modality providing insights into the cerebrovascular vessel tree that are crucial for the diagnosis and treatment of ischemic strokes, in particular in cases of large vessel occlusions (LVO). Thus, the clinical workflow greatly benefits from an automated detection of patients suffering from LVOs. This work uses convolutional neural networks for case-level classification trained with elastic deformation of the vessel tree segmentation masks to artificially augment training data. Using only masks as the input to our model uniquely allows us to apply such deformations much more aggressively than one could with conventional image volumes while retaining sample realism. The neural network classifies the presence of an LVO and the affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate that the use of the suggested augmentation enables us to train robust models even from few data sets. Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85 from a baseline value of 0.56 using no augmentation. The best performance was achieved using a 3D-DenseNet yielding an AUC of 0.87. The augmentation had positive impact in classification of the affected hemisphere as well, where the 3D-DenseNet reached an AUC of 0.93 on both sides.