MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs (2504.06897v1)

Abstract: This paper presents MedSegFactory, a versatile medical synthesis framework that generates high-quality paired medical images and segmentation masks across modalities and tasks. It aims to serve as an unlimited data repository, supplying image-mask pairs to enhance existing segmentation tools. The core of MedSegFactory is a dual-stream diffusion model, where one stream synthesizes medical images and the other generates corresponding segmentation masks. To ensure precise alignment between image-mask pairs, we introduce Joint Cross-Attention (JCA), enabling a collaborative denoising paradigm by dynamic cross-conditioning between streams. This bidirectional interaction allows both representations to guide each other's generation, enhancing consistency between generated pairs. MedSegFactory unlocks on-demand generation of paired medical images and segmentation masks through user-defined prompts that specify the target labels, imaging modalities, anatomical regions, and pathological conditions, facilitating scalable and high-quality data generation. This new paradigm of medical image synthesis enables seamless integration into diverse medical imaging workflows, enhancing both efficiency and accuracy. Extensive experiments show that MedSegFactory generates data of superior quality and usability, achieving competitive or state-of-the-art performance in 2D and 3D segmentation tasks while addressing data scarcity and regulatory constraints.

An Evaluation of "MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs"

The paper introduces "MedSegFactory," a sophisticated framework for generating high-quality paired medical images and segmentation masks using user-defined textual prompts. This framework is positioned to address the challenges in accessing annotated medical imaging data, which are typically scarce and burdened by privacy concerns.

MedSegFactory employs a dual-stream diffusion model to synthesize medical images along with their corresponding segmentation masks. This model introduces Joint Cross-Attention (JCA) to ensure alignment and mutual enhancement between image and mask pairs through a collaborative denoising process. The innovation lies in generating these pairs based on concise text prompts that describe attributes such as target labels, imaging modalities, and pathological conditions. This reduces reliance on expensive and difficult-to-acquire segmentation masks, making the framework more scalable.

In extensive experiments, MedSegFactory demonstrated the capability to produce high-quality and semantically consistent image-mask pairs. Notably, the generated data matched or exceeded state-of-the-art performance benchmarks in both 2D and 3D medical segmentation tasks. This suggests that the synthetic data can significantly enhance existing segmentation tools, addressing clinical data scarcity without infringing upon patient confidentiality.

The implications of MedSegFactory's robust framework are twofold. Practically, it paves the way for generating versatile synthetic datasets, potentially enabling broader access to training data in medical image analysis. Theoretically, it opens avenues for exploring how text-driven frameworks can further integrate into diverse medical imaging workflows, potentially enhancing efficiency and accuracy. However, as indicated in the paper, generation of high-resolution, consistent 3D medical images remains an area for further refinement.

Regarding future developments, MedSegFactory may leverage more sophisticated prompt designs or enhanced mask strategies to overcome existing limitations, like blurred boundary representation in target-free generation settings. Extending capabilities to fully model volumetric data is also necessary to bridge existing gaps in 3D medical imaging.

In summary, MedSegFactory represents a powerful tool for advancing medical imaging applications. It facilitates overcoming existing barriers due to data availability and privacy regulations, emphasizing a scalable approach for data synthesis in the medical domain.