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3D segmentation combining spatial and multi-scale features for intracranial aneurysm.

Xinfeng Zhang¹, Jie Shao¹, Xiangsheng Li²

¹School of Information Science and Technology, Beijing University of Technology, Chaoyang District, Beijing, China.

Medical Physics

|March 29, 2025

Summary

This summary is machine-generated.

This study introduces SMNet, a novel 3D segmentation network for diagnosing intracranial aneurysms from MRA scans. SMNet significantly improves diagnostic accuracy and efficiency, aiding in computer-assisted clinical interventions.

Keywords:

3D medical image segmentation channel & spatial attention convolutional neural network intracranial aneurysm multi‐scale feature extraction

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Neurosurgery Support

Background:

Traditional diagnosis of intracranial aneurysms relies on subjective and inefficient interpretation of radiological imaging by experienced doctors.
Limitations in human assessment can lead to diagnostic errors, including misdiagnosis and missed diagnoses.

Purpose of the Study:

To enhance the diagnostic efficiency for intracranial aneurysms.
To minimize misdiagnosis and missed diagnosis rates in clinical practice.

Main Methods:

Development of SMNet, a 3D segmentation network based on the U-Net architecture.
Integration of spatial and multi-scale features using Multi-scale Feature Extraction (MSE) Block and Strip Volumetric Pooling (SVP) Block.
Refinement of feature representation through Quaternary Spatial Attention (QSA) Block for improved discrimination.

Main Results:

SMNet demonstrated significant improvements in segmentation performance on both private and public (ADAM) datasets.
Achieved increases of 16.7% in Dice and 28% in MIoU compared to baseline on a private dataset.
Outperformed mainstream 3D medical image segmentation models in segmentation quality.

Conclusions:

The proposed SMNet model substantially enhances intracranial aneurysm segmentation from MRA images.
Contributes to the advancement of computer-assisted diagnosis and treatment in neurosurgery.