MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation
  towards Unannotated Modalities
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.
67526571c87c395cd2fc896e
Haoning Wu
claimed_verified
2024-12-06T09:09:19.410Z
632c7a0d1d303f5f9acf01b8
https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
Haoning Wu
user
haoningwu
67526571c87c395cd2fc896f
Ziheng Zhao
claimed_verified
2025-06-05T10:00:35.337Z
6496eae78a7c70379a512e39
/avatars/f5ab483ae93cc04b43e825dfd9440905.svg
Ziheng Zhao
user
zzh99
67526571c87c395cd2fc8970
Ya Zhang
67526571c87c395cd2fc8971
Weidi Xie
67526571c87c395cd2fc8972
Yanfeng Wang
MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation
  towards Unannotated Modalities
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.
A diffusion-based data engine, MRGen, is proposed to synthesize training samples from unannotated medical modalities using text prompts and masks, effectively extending MRI segmentation.
generative models diffusion-based data engine MRGen radiology image-text dataset MedGen-1M MRI segmentation text prompts organ mask annotations