13.03.2023The Menze group publishes a deep learning approach to predict collateral flow in stroke patients

In this work, the Menze group presents a multi-stage deep learning approach to predict collateral flow grading in stroke patients. Based on radiomic features extracted from MR perfusion data, a region of interest (RoI) detection task is formulated as a reinforcement learning problem and used to train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes.
Next, radiomic features are extracted from the obtained RoI through local image descriptors and denoising auto-encoders. Finally, a convolutional neural network and other machine learning classifiers are applied to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes. This automated deep learning approach is faster than visual inspection, eliminates grading bias and demonstrates a performance comparable to expert grading.

See Tetteh et al., Front Neurol