Fully automated unruptured intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network

Abstract

Introduction: Digital subtraction angiography (DSA) is the gold standard in detection of intracranial aneurysms, a potential life-threatening condition. Early detection, diagnosis and treatment of unruptured intracranial aneurysms (UIAs) based on DSA can effectively decrease the incidence of cerebral hemorrhage. Methods: We proposed and evaluated a novel fully automated detection and segmentation deep neural network structure to help neurologists find and contour UIAs from 2D+time DSA sequences during UIA treatment. The network structure is based on a general U-shape design for medical image segmentation and detection. The network further includes fully convolutional technique to detect aneurysms in high resolution DSA frames. In addition, a bidirectional convolutional long short-term memory (LSTM) module is introduced at each level of the network to capture the contrast medium flow change across the DSA 2D frames. The resulting network incorporates both spatial and temporal information from DSA sequences and can be trained end-to-end. Experiments: The proposed network structure was trained with DSA sequences from 347 patients with presence of UIAs. After that, the system was evaluated on an independent test set with 947 DSA sequences from 146 patients. Results: 316 out of 354 (89.3%) aneurysms were successfully detected, which corresponds to a more clinical related blood vessel level sensitivity 94.3% at a false positive rate 3.77 per sequence. The system runs less than one second per sequence with an average Dice coefficient score 0.533.