title: MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation
    towards Unannotated Modalities
summary: 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.
paper_authors._id: 67526571c87c395cd2fc896e
paper_authors.name: Haoning Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T09:09:19.410Z
paper_authors._id: 632c7a0d1d303f5f9acf01b8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
paper_authors.fullname: Haoning Wu
paper_authors.type: user
paper_authors.user: haoningwu
paper_authors._id: 67526571c87c395cd2fc896f
paper_authors.name: Ziheng Zhao
paper_authors._id: 67526571c87c395cd2fc8970
paper_authors.name: Ya Zhang
paper_authors._id: 67526571c87c395cd2fc8971
paper_authors.name: Weidi Xie
paper_authors._id: 67526571c87c395cd2fc8972
paper_authors.name: Yanfeng Wang
paper_title: MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation
    towards Unannotated Modalities
paper_summary: 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.
paper_ai_keywords: generative models diffusion-based data engine text prompts MR images MRI segmentation