MRGen: Segmentation Data Engine for Underrepresented MRI Modalities

TL;DR

This paper introduces MRGen, a diffusion-based generative model and dataset for synthesizing MRI images to improve segmentation in underrepresented modalities with scarce annotations.

Contribution

The paper presents a new large-scale dataset and a controllable generative model to synthesize realistic MRI images, enhancing segmentation performance in low-resource domains.

Findings

MRGen improves segmentation accuracy on unannotated MRI modalities.

The dataset includes extensive metadata and a subset with pixel-wise annotations.

Synthetic data generated by MRGen significantly boosts model training.

Abstract

Training medical image segmentation models for rare yet clinically important imaging modalities is challenging due to the scarcity of annotated data, and manual mask annotations can be costly and labor-intensive to acquire. This paper investigates leveraging generative models to synthesize data, for training segmentation models for underrepresented modalities, particularly on annotation-scarce MRI. Concretely, our contributions are threefold: (i) we introduce MRGen-DB, a large-scale radiology image-text dataset comprising extensive samples with rich metadata, including modality labels, attributes, regions, and organs information, with a subset featuring pixel-wise mask annotations; (ii) we present MRGen, a diffusion-based data engine for controllable medical image synthesis, conditioned on text prompts and segmentation masks. MRGen can generate realistic images for diverse MRI modalities lacking mask annotations, facilitating segmentation training in low-source domains; (iii) extensive experiments across multiple modalities demonstrate that MRGen significantly improves segmentation performance on unannotated modalities by providing high-quality synthetic data. We believe that our method bridges a critical gap in medical image analysis, extending segmentation capabilities to scenarios that are challenging to acquire manual annotations. The codes, models, and data will be publicly available at https://haoningwu3639.github.io/MRGen/