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Vessel-aware aneurysm detection using multi-scale deformable 3D attention.

Alberto M Ceballos-Arroyo1, Hieu T Nguyen1, Fangrui Zhu1

  • 1Northeastern University.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 14, 2025
PubMed
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This study introduces a novel 3D neural network for automated detection of intracranial aneurysms (IAs) in CT scans, significantly improving accuracy and reducing the time needed for diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurosurgery

Background:

  • Manual detection of intracranial aneurysms (IAs) in computed tomography (CT) scans is challenging due to small aneurysm size and high false positive rates.
  • Automating IA detection is crucial for efficient and accurate clinical diagnosis.

Purpose of the Study:

  • To develop and validate a 3D, multi-scale neural architecture for automated detection and segmentation of intracranial aneurysms (IAs) in CT scans.
  • To improve the sensitivity and reduce false positives in IA detection compared to existing methods.

Main Methods:

  • Proposed a 3D, multi-scale neural network incorporating a deformable attention mechanism.
  • Utilized vessel distance maps and 3D convolutional features for detection.
  • Reformulated segmentation as bounding cuboid prediction with binary cross-entropy and localization losses.
Keywords:
Aneurysm detectionCT angiographyDeep learning

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Main Results:

  • Achieved high sensitivity rates (91.3%/97.0%/74.1%) across three validation sets with low false positive rates.
  • Demonstrated approximately 80% sensitivity for detecting small aneurysms.
  • Expert review indicated the model primarily missed aneurysms in unusual locations.

Conclusions:

  • The developed 3D neural architecture effectively detects intracranial aneurysms in CT scans.
  • The model shows promise for clinical application, aiding in faster and more accurate diagnosis.
  • Further refinement may improve detection of aneurysms in atypical locations.