nnDetection for Intracranial Aneurysms Detection and Localization
Maysam Orouskhani Negar Firoozeh Shaojun Xia Mahmud Mossa-Basha Chengcheng Zhu
Intracranial aneurysms are a commonly occurring and life-threatening
condition, affecting approximately 3.2% of the general population.
Consequently, detecting these aneurysms plays a crucial role in their
management. Lesion detection involves the simultaneous localization and
categorization of abnormalities within medical images. In this study, we
employed the nnDetection framework, a self-configuring framework specifically
designed for 3D medical object detection, to detect and localize the 3D
coordinates of aneurysms effectively. To capture and extract diverse features
associated with aneurysms, we utilized TOF-MRA and structural MRI, both
obtained from the ADAM dataset. The performance of our proposed deep learning
model was assessed through the utilization of free-response receiver operative
characteristics for evaluation purposes. The model's weights and 3D prediction
of the bounding box of TOF-MRA are publicly available at
https://github.com/orouskhani/AneurysmDetection.