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Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data

Weiwei Gao¹, Xiaofeng Li², Yanwei Wang³

¹College of Information and Technology, Wenzhou Business College, Wenzhou, China.

Frontiers in Public Health

|April 25, 2022

Summary

This summary is machine-generated.

This study introduces a novel 3D multimodal medical image segmentation algorithm using deep reinforcement learning and big data analytics to improve diagnostic accuracy. The method effectively enhances image quality and segmentation performance, achieving over 95% accuracy.

Keywords:

deep reinforcement learning high-frequency signal component medical image segmentation three-dimensional multimodal wavelet shrinkage

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Area of Science:

Medical Imaging
Artificial Intelligence
Data Science

Background:

Segmented 3D multimodal medical images often suffer from relative overlap and low signal-to-noise ratio (SNR), hindering effective medical diagnosis.
Existing segmentation methods face challenges in accurately processing complex 3D multimodal data.

Purpose of the Study:

To propose a 3D multimodal medical image segmentation algorithm that overcomes limitations of relative overlap and low SNR.
To enhance the accuracy and effectiveness of medical image diagnosis through advanced AI techniques.

Main Methods:

A wavelet shrinkage algorithm using Bayesian maximum a posteriori estimation and an improved wavelet threshold function was employed for denoising.
A deep reinforcement learning-based DRD U-Net model was constructed, incorporating residual modules and multiscale context feature extraction.
The algorithm utilizes a reward and punishment mechanism for 3D multimodal medical image segmentation.

Main Results:

The proposed algorithm demonstrated effective segmentation performance on LIDC-IDRI, SCR, and DeepLesion datasets.
Structural similarity reached 98% with SNR maintained between 55-60 dB after 250 iterations.
Relative overlap and accuracy exceeded 95%, indicating superior overall segmentation performance.

Conclusions:

The developed algorithm significantly improves 3D multimodal medical image segmentation by leveraging deep reinforcement learning and big data analytics.
The findings suggest a promising approach for enhancing medical image analysis and diagnostic capabilities.