FOSCU: Feasibility of Synthetic MRI Generation via Duo-Diffusion Models for Enhancement of 3D U-Nets in Hepatic Segmentation
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Medical image segmentation faces fundamental challenges including restricted access, costly annotation, and data shortage to clinical datasets through Picture Archiving and Communication Systems (PACS). These systemic barriers significantly impede the development of robust segmentation algorithms. To address these challenges, we propose FOSCU, which integrates Duo-Diffusion, a 3D latent diffusion model with ControlNet that simultaneously generates high-resolution, anatomically realistic synthetic MRI volumes and corresponding segmentation labels, and an enhanced 3D U-Net training pipeline. Duo-Diffusion employs segmentation-conditioned diffusion to ensure spatial consistency and precise anatomical detail in the generated data. Experimental evaluation on 720 abdominal MRI scans shows that models trained with combined real and synthetic data yield a mean Dice score gain of 0.67% over those using only real data, and achieve a 36.4% reduction in Fr'echet Inception Distance (FID), reflecting enhanced image fidelity.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 19 of its 29 references that are in the map. First posted on arXiv 31 Mar 2026.
Builds on
19 of 29 references.
Closest ancestors · 2
Same direction, newest first
- 2022Diffusion Probabilistic Models beat GANs on Medical Images14 Dec
- 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
Other references · 17
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5 more
- 2017Road Extraction by Deep Residual U-Netcited by 333
- 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
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Citing papers per year: 2026: 2.
2 papers.
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Close in wording, no citation either way
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- 20243D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
- 2026Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models
- 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
- 2026Improving Medical Image Generative Models with Fr'echet Distance Loss
- 2025Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation