Deep Learning–Based Detection of Intracranial Aneurysms in 3D TOF-MRA

Flowchart of the pipeline. A, Preprocessing is performed with 4 different models. The dataset is split into test, training, and validation sets. B, Inference is performed with the convolutional neural network DeepMedic with a 2-pathway architecture. The number of feature maps and their size is depicted as number × size. The + depicts the addition of the 2 preceding layers, which adds an additional nonlinearity and reduces the number of weights.¹⁸ The diagram is based on the depiction in the DeepMedic documentation. (Modified from Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis 2017;36:61–78 under CC-BY4 license).³² C, Thresholding is applied to the resulting segmentation and evaluated with different metrics. L_N indicates layers in the normal resolution pathway, L_L indicates layers in the low resolution pathway.