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

Sichtermann T, Faron A, Sijben R, Teichert N, Freiherr J, Wiesmann M (2019)

Publication Status: Published

Publication Type: Journal article

Publication year: 2019

Journal

American Journal of Neuroradiology American Society of Neuroradiology

Publisher: AMER SOC NEURORADIOLOGY

Book Volume: 40

Pages Range: 25-32

Journal Issue: 1

DOI: 10.3174/ajnr.A5911

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 3-7mmand 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.

Authors with CRIS profile

Jessica Freiherr

Involved external institutions

Universitätsklinikum Aachen (UKA) DE Germany (DE) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE)

How to cite

APA:

Sichtermann, T., Faron, A., Sijben, R., Teichert, N., Freiherr, J., & Wiesmann, M. (2019). Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA. American Journal of Neuroradiology, 40(1), 25-32. https://dx.doi.org/10.3174/ajnr.A5911

MLA:

Sichtermann, Thorsten, et al. "Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA." American Journal of Neuroradiology 40.1 (2019): 25-32.

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