Abstract
BACKGROUND AND PURPOSE: The rupture of an intracranial aneurysm is a serious incident, causing subarachnoid hemorrhage associated with high fatality and morbidity rates. Because the demand for radiologic examinations is steadily growing, physician fatigue due to an increased workload is a real concern and may lead to mistaken diagnoses of potentially relevant findings. Our aim was to develop a sufficient system for automated detection of intracranial aneurysms. MATERIALS AND METHODS: In a retrospective study, we established a system for the detection of intracranial aneurysms from 3D TOF-MRA data. The system is based on an open-source neural network, originally developed for segmentation of anatomic structures in medical images. Eighty-five datasets of patients with a total of 115 intracranial aneurysms were used to train the system and evaluate its performance. Manual annotation of aneurysms based on radiologic reports and critical revision of image data served as the reference standard. Sensitivity, false-positives per case, and positive predictive value were determined for different pipelines with modified pre- and postprocessing. RESULTS: The highest overall sensitivity of our system for the detection of intracranial aneurysms was 90% with a sensitivity of 96% for aneurysms with a diameter of 3-7 mm and 100% for aneurysms of >7 mm. The best location-dependent performance was in the posterior circulation. Pre- and postprocessing sufficiently reduced the number of false-positives. CONCLUSIONS: Our system, based on a deep learning convolutional network, can detect intracranial aneurysms with a high sensitivity from 3D TOF-MRA data.