nnDetection for Intracranial Aneurysms Detection and Localization

TL;DR

This paper presents a deep learning approach using the nnDetection framework to accurately detect and localize intracranial aneurysms in 3D medical images, aiding early diagnosis and treatment planning.

Contribution

It introduces a novel application of the nnDetection framework for intracranial aneurysm detection using multimodal MRI data, demonstrating effective localization capabilities.

Findings

Achieved high detection accuracy on the ADAM dataset

Successfully localized aneurysms in 3D MRI scans

Model weights and predictions are publicly available

Abstract

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.