Abstract
Purpose
Intracranial aneurysm detection from 3D Time-Of-Flight Magnetic Resonance Angiography images is a problem of increasing clinical importance. Recently, a streak of methods have shown promising performance by using segmentation neural networks. However, these methods may be less relevant in a clinical settings where diagnostic decisions rely on detecting objects rather than their segmentation.
Methods
We introduce a 3D single-stage object detection method tailored for small object detection such as aneurysms. Our anchor-free method incorporates fast data annotation, adapted data sampling and generation to address class imbalance problem, and spherical representations for improved detection.
Results
A comprehensive evaluation was conducted, comparing our method with the state-of-the-art SCPM-Net, nnDetection and nnUNet baselines, using two datasets comprising 402 subjects. The evaluation used adapted object detection metrics. Our method exhibited comparable or superior performance, with an average precision of 78.96%, sensitivity of 86.78%, and 0.53 false positives per case.
Conclusion
Our method significantly reduces the detection complexity compared to existing methods and highlights the advantages of object detection over segmentation-based approaches for aneurysm detection. It also holds potential for application to other small object detection problems.
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Code availability
Both our code and annotations used for dataset [20] are available at: https://gitlab.inria.fr/yassis/DeepAneDet.
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This work was co-funded by the Grand-Est Region and the University Hospital of Nancy, France.
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Assis, Y., Liao, L., Pierre, F. et al. Intracranial aneurysm detection: an object detection perspective. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03132-z
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DOI: https://doi.org/10.1007/s11548-024-03132-z