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DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model.

Zheyuan Zhang, Lanhong Yao, Bin Wang

IEEE Transactions on Medical Imaging

|March 3, 2025

Summary

This summary is machine-generated.

DiffBoost generates realistic synthetic medical images using controllable diffusion models, enhancing segmentation accuracy across various datasets. This approach addresses data scarcity for improved deep learning in medical imaging.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

High-quality, large-scale data are essential for robust deep learning in medical applications, but data scarcity poses a significant challenge.
Overfitting and poor generalization performance can result from insufficient or low-quality medical datasets.

Purpose of the Study:

To introduce DiffBoost, a novel approach using controllable diffusion models for synthesizing realistic and diverse medical images.
To address the challenge of limited high-quality labeled data in medical imaging for deep learning tasks.

Main Methods:

Leveraging diffusion probabilistic models to generate synthetic medical images guided by edge information.
Ensuring synthesized samples adhere to medically relevant constraints and preserve underlying data structures.
Utilizing random sampling for generating an arbitrary number of diverse synthetic images.

Main Results:

Achieved significant improvements in medical image segmentation tasks on Ultrasound breast (+13.87%), CT spleen (+0.38%), and MRI prostate (+7.78%) datasets.
Demonstrated the effectiveness of DiffBoost over baseline segmentation methods.
Showcased the feasibility of a text-guided diffusion model for general medical image segmentation.

Conclusions:

DiffBoost effectively enhances medical image segmentation performance by generating high-quality synthetic data.
The proposed method offers a viable solution to the challenge of data scarcity in medical deep learning.
This work presents the first text-guided diffusion model applicable to general medical image segmentation.