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arXiv · 2509.01330

Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentation

Fuyou Mao·Beining Wu·Yanfeng Jiang·Han Xue·Yan Tang·Hao Zhang
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Abstract

Ambiguity in medical image segmentation calls for models that capture full conditional distributions rather than a single point estimate. We present Prior-Guided Residual Diffusion (PGRD), a diffusion-based framework that learns voxel-wise distributions while maintaining strong calibration and practical sampling efficiency. PGRD embeds discrete labels as one-hot targets in a continuous space to align segmentation with diffusion modeling. A coarse prior predictor provides step-wise guidance; the diffusion network then learns the residual to the prior, accelerating convergence and improving calibration. A deep diffusion supervision scheme further stabilizes training by supervising intermediate time steps. Evaluated on representative MRI and CT datasets, PGRD achieves higher Dice scores and lower NLL/ECE values than Bayesian, ensemble, Probabilistic U-Net, and vanilla diffusion baselines, while requiring fewer sampling steps to reach strong performance.

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Fuyou Mao, Beining Wu, Yanfeng Jiang, Han Xue, Yan Tang, Hao Zhang. 2026-01-31. Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentation. https://arxiv.org/abs/2509.01330

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