FOSCU: Feasibility of Synthetic MRI Generation via Duo-Diffusion Models for Enhancement of 3D U-Nets in Hepatic Segmentation

Authors:Youngung Han, Kyeonghun Kim, Seoyoung Ju, Yeonju Jean, Minkyung Cha, Seohyoung Park, Hyeonseok Jung, Nam-Joon Kim, Woo Kyoung Jeong, Ken Ying-Kai Liao, Hyuk-Jae Lee

View PDF HTML (experimental)

Abstract: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 Fréchet Inception Distance (FID), reflecting enhanced image fidelity.

Comments: 10 pages, 5 figures. Accepted at IEEE APCCAS 2025

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2603.29343 [cs.CV]

(or arXiv:2603.29343v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2603.29343

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Kyeonghun Kim [view email] [v1] Tue, 31 Mar 2026 07:09:51 UTC (710 KB)