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LesionLab enhances medical image segmentation by creating synthetic data, addressing data scarcity and imbalance issues. This novel framework improves lesion segmentation model robustness and accuracy.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate medical image segmentation is crucial for diagnosis and treatment planning.
Data scarcity and sample imbalance hinder the development of robust lesion segmentation models.
Existing synthetic data augmentation methods often fail to generate high-quality lesion-containing samples.

Purpose of the Study:

To introduce LesionLab, a novel framework for synthesizing medical image-mask pairs.
To augment existing datasets, creating more balanced and diverse training data for lesion segmentation.
To improve the quality and controllability of synthetic medical image data generation.

Main Methods:

Phenotype-guided text prompts are designed by clustering radiomic features to capture complex lesion characteristics.

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A dual-check quality control mechanism using foundation model priors assesses sample quality and hardness.

Synthetic data generation is controlled to precisely transform lesion-free to lesion-containing samples.

Main Results:

LesionLab effectively augments datasets, leading to more balanced and diverse training data.
The phenotype-guided prompts enhance the controllability of synthetic data generation.
The dual-check mechanism filters low-quality samples and prioritizes challenging cases, improving model training.
Experiments on three public datasets show LesionLab outperforms existing synthetic data augmentation methods.

Conclusions:

LesionLab provides a superior approach to synthetic data augmentation for medical lesion segmentation.
The framework addresses key challenges of data scarcity and sample imbalance effectively.
LesionLab contributes to the development of more robust and accurate medical image segmentation models.