MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation
  towards Unannotated Modalities
Authors: name: Haoning Wu, name: Ziheng Zhao, name: Ya Zhang, name: Weidi Xie, name: Yanfeng Wang

Medical image segmentation has recently demonstrated impressive progress with
deep neural networks, yet the heterogeneous modalities and scarcity of mask
annotations limit the development of segmentation models on unannotated
modalities. This paper investigates a new paradigm for leveraging generative
models in medical applications: controllably synthesizing data for unannotated
modalities, without requiring registered data pairs. Specifically, we make the
following contributions in this paper: (i) we collect and curate a large-scale
radiology image-text dataset, MedGen-1M, comprising modality labels,
attributes, region, and organ information, along with a subset of organ mask
annotations, to support research in controllable medical image generation; (ii)
we propose a diffusion-based data engine, termed MRGen, which enables
generation conditioned on text prompts and masks, synthesizing MR images for
diverse modalities lacking mask annotations, to train segmentation models on
unannotated modalities; (iii) we conduct extensive experiments across various
modalities, illustrating that our data engine can effectively synthesize
training samples and extend MRI segmentation towards unannotated modalities.