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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

13.02.2020 | Original Article

Deep learning for automated cerebral aneurysm detection on computed tomography images

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 4/2020

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Abstract

Purpose

Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medical centers and different machines to develop and evaluate an automatic detection model.

Methods

In this study, we have introduced a deep learning model, the faster RCNN model, in order to develop a tool for automatic detection of aneurysms from medical images. The inputs of the model were 2D nearby projection (NP) images from 3D CTA, which were made by the NP method proposed in this study. This method made aneurysms clearly visible on images and improved the model’s performance. The study included 311 patients with 352 aneurysms, selected from three hospitals, and 208 and 103 of these patients, respectively, were randomly selected to train and test the models.

Results

The sensitivity of the trained model was 91.8%. For aneurysm sizes larger than 3 mm, the sensitivity of successful aneurysm detection was 96.7%. We achieved state-of-the-art sensitivity for > 3 mm aneurysms. The sensitivities also indicated that there was no significant difference among aneurysms at different locations in the body. Computing time for the detection process was less than 25 s per case.

Conclusions

We successfully developed a deep learning model that can automatically detect aneurysms. The model performed well for aneurysms of different sizes or in different locations. This finding indicates that the deep learning model has the potential to vastly improve clinician performance by providing automated aneurysm detection.

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Titel: Deep learning for automated cerebral aneurysm detection on computed tomography images
Publikationsdatum: 13.02.2020
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 4/2020
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-020-02121-2

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Abstract

Purpose

Methods

Results

Conclusions

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