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Updated: Oct 26, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Cerebral aneurysm image segmentation based on multi-modal convolutional neural network.

Chengjie Meng¹, Debiao Yang², Dan Chen³

¹Department of Neurosurgery, Yancheng First Peoples' Hospital, Yancheng 224005, China.

Computer Methods and Programs in Biomedicine

|July 29, 2021

Summary

This summary is machine-generated.

This study introduces a novel multi-modal convolutional neural network for segmenting cerebral aneurysms in computed tomography angiography (CTA) images. The method enhances segmentation accuracy, aiding in diagnosis and treatment planning.

Keywords:

Algorithm Cerebral aneurysm Computed tomography angiography Convolutional neural network Image segmentation

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical imaging analysis
Neurosurgery support
Artificial intelligence in healthcare

Background:

Accurate segmentation of cerebral aneurysms in computed tomography angiography (CTA) is crucial for effective diagnosis and treatment planning.
Existing segmentation methods may have limitations in precision and detail.

Purpose of the Study:

To evaluate a novel, more helpful image segmentation method for cerebral aneurysms using multi-modal CTA data.
To enhance the accuracy of cerebral aneurysm and adjacent vessel extraction.

Main Methods:

Utilized Gaussian and Laplace filtering on original CTA images to create multi-modal input data.
Employed multiple parallel convolutional neural networks for multi-modal image segmentation.
Fused segmentation results using linear regression for final extraction of aneurysms and vessels.

Main Results:

Successfully extracted cerebral aneurysms and adjacent vessels with high accuracy.
Optimal segmentation performance was achieved with a threshold value around 0.95.
Dice, accuracy, and recall rates varied across different extraction method combinations.

Conclusions:

A multi-modal convolutional neural network approach significantly improves cerebral aneurysm segmentation accuracy.
Multi-modal processing of brain CTA images is a promising strategy for enhanced neuroimaging analysis.