EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation

Caiyang Yu ¹, Yixi Wang ¹, Chenwei Tang ¹

⁰College of Computer Science, Sichuan University, Chengdu, 610065, China.

Computers in Biology and Medicine +

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November 4, 2024

View abstract on PubMed

Summary

This study introduces EU-Net, an automated algorithm for segmenting medical images. It uses differential evolution to optimize U-Net architectures, improving diagnostic accuracy and reducing manual effort in clinical settings.

Area Of Science

Medical Imaging
Artificial Intelligence
Computer Vision

Background

Manual segmentation of medical images is time-consuming and error-prone.
U-Net automates segmentation but requires expertise in neural network design.
Optimizing U-Net architectures is crucial for accurate medical image analysis.

Purpose Of The Study

To develop an automatic U-Net Neural Architecture Search (NAS) algorithm for medical image segmentation.
To assist physicians in diagnosis by enhancing the accuracy of image interpretation.
To automate the search for optimal U-Net architectures without requiring specialized expertise.

Main Methods

Proposed an automatic U-Net NAS algorithm named EU-Net, utilizing the differential evolutionary (DE) algorithm.
Implemented a variable-length strategy for automatic architecture search.
Incorporated DE's crossover, mutation, and selection strategies for exploration-exploitation balance.
Introduced block-based and layer-based structures in encoding/decoding phases for optimization.

Main Results

EU-Net demonstrated superior performance on CHAOS and BUSI medical image segmentation datasets.
The algorithm successfully automated U-Net architecture search, reducing the need for expert knowledge.
Achieved at least a 6% improvement in the mean Intersection over Union (mIoU) metric compared to the original U-Net.

Conclusions

EU-Net effectively automates the optimization of U-Net architectures for medical image segmentation.
The proposed method enhances diagnostic accuracy and efficiency in clinical practice.
EU-Net offers a promising solution for complex medical image analysis tasks.

Keywords:

Differential evolution Medical image segmentation Neural architecture search U-Net