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CAN: Context-assisted full Attention Network for brain tissue segmentation.

Zhan Li¹, Chunxia Zhang¹, Yongqin Zhang¹

¹School of Information Science and Technology, Northwest University, 710127, Xi'an, China.

Medical Image Analysis

|December 31, 2022

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This summary is machine-generated.

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This study introduces the Context-assisted full Attention Network (CAN) for improved brain Magnetic Resonance Imaging (MRI) segmentation. The CAN effectively integrates 2D and 3D MRI data, enhancing diagnostic accuracy for brain disorders.

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Brain tissue segmentation is crucial for diagnosing neurological disorders.
Current 2D and 3D segmentation methods for brain MRI have limitations in accuracy and speed.
Existing U-Net architectures often use fully symmetric structures, which may not be optimal.

Purpose of the Study:

To develop a novel Context-assisted full Attention Network (CAN) for brain MRI segmentation.
To address the time complexity of 3D segmentation and accuracy issues of 2D segmentation.
To improve brain MRI segmentation by integrating both 2D and 3D data.

Main Methods:

The proposed CAN integrates 2D slices with 3D contextual skull and brain slices.
Input data is encoded using DenseNet and decoded by a custom full attention network.

Keywords:

Attention Convolutional neural network Medical image Semantic segmentation

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The method was validated on three datasets: PWML, dHCP2017, and MALC2012.

Main Results:

The CAN demonstrated effectiveness in brain MRI segmentation tasks.
Integration of 2D and 3D data improved segmentation performance.
The network achieved promising results on both collected and public datasets.

Conclusions:

The Context-assisted full Attention Network (CAN) offers a promising approach for brain MRI segmentation.
CAN effectively combines 2D and 3D information for enhanced segmentation accuracy and efficiency.
This method has the potential to aid in the diagnosis of brain disorders.