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

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Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement

Zhenghua Xu¹, Shengxin Wang¹, Gang Xu²

¹State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China.

Computers in Biology and Medicine

|December 29, 2023

Summary

This summary is machine-generated.

This study introduces an Adaptive Sequence-length based Deep Reinforcement Learning (ASDRL) model for automatic data augmentation in medical imaging. The ASDRL-AutoAug model enhances medical image segmentation accuracy by improving augmentation validity and adequacy.

Keywords:

Adaptive Sequence-length Deep Reinforcement Learning Automatic Data Augmentation Medical image segmentation Reward function

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Area of Science:

Medical Image Analysis
Artificial Intelligence
Computer Vision

Background:

Current deep reinforcement learning methods for image augmentation show limitations in medical image analysis.
Existing approaches often result in invalid or insufficient data augmentation, impacting model performance.

Purpose of the Study:

To propose a novel Adaptive Sequence-length based Deep Reinforcement Learning (ASDRL) model for Automatic Data Augmentation (AutoAug) in intelligent medical image analysis.
To enhance the accuracy and adequacy of data augmentation for medical image segmentation tasks.

Main Methods:

Developed a novel ASDRL model incorporating a refined reward function to accurately assess augmentation transformation validity.
Introduced an intelligent automatic stopping mechanism (ASM) to ensure augmentation adequacy and optimal model performance improvement.

Main Results:

ASDRL-AutoAug significantly outperforms state-of-the-art data augmentation methods on three medical image segmentation datasets.
The proposed reward function and ASM are crucial for superior performance, offering more accurate transformation evaluation and controlled augmentation.
Demonstrated adaptability to varying image sequence lengths and generalizability across different segmentation models.

Conclusions:

ASDRL-AutoAug represents a significant advancement in automatic data augmentation for medical image analysis.
The model effectively addresses challenges of invalid and insufficient augmentation, leading to improved medical image segmentation.
The approach is adaptive, generalizable, and provides a more robust solution for intelligent medical imaging.