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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation.

Chao Ma¹, Yuan Gu², Ziyang Wang³

¹Mianyang Visual Object Detection and Recognition Engineering Center, Mianyang, China.

Journal of Imaging Informatics in Medicine

|April 23, 2024

Summary

A new TriConvUNeXt model enhances biomedical image segmentation using a lightweight convolutional neural network (CNN). This approach achieves competitive results with significantly lower computational costs compared to existing methods.

Keywords:

Biomedical image segmentation Convolutional Neural Network Depthwise convolution Image semantic segmentation

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Biomedical image segmentation is crucial for clinical diagnosis and treatment planning.
Self-attention networks offer high performance but are computationally expensive.
Modified Convolutional Neural Networks (CNNs) show promise but struggle with channel interaction.

Purpose of the Study:

To design a lightweight network block for improved feature learning in biomedical image segmentation.
To address the limitations of modified CNNs in capturing channel interactions.
To develop an efficient and effective CNN-based model for biomedical image segmentation.

Main Methods:

Introduced a multi-convolutional, multi-scale convolutional network block (MSConvNeXt) integrating depthwise, deformable, and dilated CNNs.
Incorporated channel shuffling for dynamic feature map fusion.
Deployed the MSConvNeXt block within a U-shape symmetrical encoder-decoder network named TriConvUNeXt.

Main Results:

TriConvUNeXt achieved a 1% higher Dice-Coefficient than UNet and TransUNet on a public benchmark dataset.
The model demonstrated significantly lower computational costs: 81% and 97% less than baseline methods.
Comprehensive evaluation confirmed competitive segmentation performance with reduced computational demands.

Conclusions:

The proposed TriConvUNeXt model offers an efficient and effective solution for biomedical image segmentation.
Lightweight CNNs, enhanced with multi-scale and channel interaction mechanisms, can rival Transformer-based approaches.
The publicly available implementation facilitates further research and application in clinical settings.