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OTO-Net: An Automated MRA Image Segmentation Network for Intracranial Aneurysms.

Jianming Ye1, Xiaomei Xu2, Liuyi Li2

  • 1First Affiliated Hospital, Gannan Medical University, Ganzhou, China.

Computational Intelligence and Neuroscience
|April 25, 2022
PubMed
Summary
This summary is machine-generated.

A new One-Two-One Fully Convolutional Network (OTO-Net) accurately segments intracranial aneurysms in MRA images, improving detection and reducing variability in clinical assessments.

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Area of Science:

  • Neurosurgery
  • Radiology
  • Medical Imaging

Background:

  • Intracranial aneurysms are cerebral blood vessel dilations posing significant mortality and morbidity risks due to potential brain bleeding.
  • Accurate detection and segmentation of intracranial aneurysms from Magnetic Resonance Angiography (MRA) are critical for patient management.
  • Current manual segmentation methods suffer from interobserver variability, impacting aneurysm assessment and growth tracking.

Purpose of the Study:

  • To develop and evaluate a novel automated segmentation method for intracranial aneurysms in MRA images.
  • To address the limitations of existing automated methods in handling the diverse appearance of intracranial aneurysms.
  • To improve the accuracy and reliability of intracranial aneurysm segmentation in clinical practice.

Main Methods:

  • A novel One-Two-One Fully Convolutional Network (OTO-Net) was proposed for automated intracranial aneurysm segmentation.
  • The OTO-Net architecture integrates downsampling, upsampling, and skip connections for comprehensive feature extraction.
  • Loss ensemble was employed as the objective function to enhance network training efficiency.

Main Results:

  • The OTO-Net achieved high automated segmentation accuracy, reaching 98.37% on a public dataset and 97.86% on a private dataset.
  • Excellent performance was demonstrated by average surface distances of 1.081 and 0.753, and Dice similarity coefficients of 0.9721 and 0.9813.
  • Low Hausdorff distances (0.578 and 0.642) further indicate the model's precision in segmenting intracranial aneurysms.

Conclusions:

  • The proposed OTO-Net demonstrates superior performance in automated intracranial aneurysm segmentation from MRA images.
  • This novel deep learning approach offers a reliable and accurate alternative to manual segmentation, reducing interobserver variability.
  • OTO-Net has the potential to significantly aid clinicians in the accurate detection and assessment of intracranial aneurysms.