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Deep learning for intracranial aneurysm segmentation using CT angiography.

Huizhong Zheng1, Xinfeng Liu2, Zhenxing Huang1

  • 1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces a two-stage deep learning method for detecting small brain aneurysms in CT angiography scans. The approach significantly improves accuracy and reduces detection time by over 50%.

Keywords:
CT angiographyaneurismsdeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurosurgery

Background:

  • Cerebral aneurysms pose a significant health risk.
  • Accurate detection of small aneurysms (4-10 mm) is crucial for timely intervention.
  • Current detection methods may face challenges with small lesion sizes.

Purpose of the Study:

  • To develop and evaluate a two-stage deep learning model for accurate detection of small cerebral aneurysms.
  • To improve the efficiency of aneurysm detection in computed tomography angiography (CTA) images.
  • To assess the performance of the model against expert manual segmentation.

Main Methods:

  • A two-stage deep learning approach was implemented, featuring a head region selection (HRS) algorithm followed by an adaptive 3D nnU-Net.
  • The study included data from 956 patients across 6 hospitals and a public dataset from 6 different CT scanners.
  • Performance was evaluated using Dice scores and compared against expert-generated segmentations.

Main Results:

  • The deep learning model achieved an Area Under the Curve (AUC) exceeding 79% across all datasets.
  • Specific datasets showed precision of 85.2% and AUC of 87.6%.
  • The inclusion of HRS reduced inference time by over 50% compared to direct inference.

Conclusions:

  • The developed deep learning method accurately segments small cerebral aneurysms by automatically localizing relevant brain regions.
  • This approach significantly accelerates aneurysm inference time, offering a more efficient diagnostic tool.
  • The model demonstrates promising performance for clinical application in CTA-based aneurysm detection.