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

TL;DR

This paper introduces FOSCU, a method that uses Duo-Diffusion models to generate synthetic MRI data and labels, improving hepatic segmentation by augmenting limited real datasets.

Contribution

The paper presents a novel Duo-Diffusion approach for simultaneous high-quality synthetic MRI and label generation, enhancing 3D U-Net training for hepatic segmentation.

Findings

Synthetic data improved Dice score by 0.67%

Achieved 36.4% reduction in FID, indicating better image quality

Combined real and synthetic data outperformed real data alone

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 Fr\'echet Inception Distance (FID), reflecting enhanced image fidelity.