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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Medical image segmentation model based on triple gate MultiLayer perceptron.

Jingke Yan¹, Xin Wang^2,3,4, Jingye Cai⁵

¹Guilin University of Electronic Technology, School of Marine Engineering, Beihai, 536000, China.

Scientific Reports

|April 13, 2022

Summary

This study introduces the Triple Gate MultiLayer Perceptron U-Net (TGMLP U-Net) for medical image segmentation. The novel model effectively captures long-distance dependencies and precise positional information, outperforming existing methods.

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Deep learning for medical image-assisted diagnosis is crucial due to limited resources and rising medical needs.
Existing Convolutional Neural Network (CNN) and Transformer models for medical image segmentation have limitations in capturing long-range dependencies or incur high computational costs.
Accurate segmentation requires models that can handle feature dependencies and positional information effectively.

Purpose of the Study:

To develop an advanced medical image segmentation model addressing limitations of current CNN and Transformer approaches.
To introduce the Triple Gate MultiLayer Perceptron U-Net (TGMLP U-Net) for improved feature dependency and positional encoding in medical images.
To enhance the efficiency and accuracy of deep learning-based medical image segmentation.

Main Methods:

Proposed the Triple Gate MultiLayer Perceptron (TGMLP) module, incorporating a Triple MLP for encoding spatial and positional information with reduced overhead.
Introduced a Local Priors and Global Perceptron module to model global dependencies and leverage multi-scale convolutions for local context.
Developed a Gate-controlled Mechanism to improve learning of position embeddings, especially with limited medical image data.

Main Results:

The TGMLP U-Net demonstrated superior performance compared to state-of-the-art models across most evaluation metrics.
The model effectively captured long-distance feature dependencies and precise 3D positional information.
Experimental results validated the model's excellent performance in segmenting medical images.

Conclusions:

The proposed TGMLP U-Net offers a significant advancement in medical image segmentation.
The model's architecture effectively overcomes the limitations of traditional CNN and Transformer models.
TGMLP U-Net shows great potential for practical applications in medical image-assisted diagnosis.