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LSR-Diff: A Diffusion Model Synthesizing Level Set Representations for Reliable Segmentation of Medical Images With Ambiguous Edges

Wenbo Gao Haoyu Cao J. Cheung Yunyun Yang
Jul 2026 · IEEE Transactions on Image Processing · Vol 35, pp. 7277-7292 · 0 citations · 58 references
Medicine Computer Science

Abstract

Accurate boundary segmentation is critical for high-stakes applications such as disease diagnosis, yet remains challenging due to complex topology, boundary ambiguity, and annotation uncertainty. Diffusion Probabilistic Models (DPMs) generate multiple masks with inherent uncertainty, enhancing boundary delineation compared to deterministic models. However, most existing DPM based segmentation approaches learn discrete binary masks, conflicting with the continuous diffusion process and leading to hard-to-learn degradation during noise addition. Moreover, the prevailing approach of averaging stochastic predictions and applying a fixed threshold disregards structural consistency, often leading to imprecise boundaries, isolated artifacts, and holes. To address these challenges, we propose the <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>evel <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>et <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>epresentation <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>iffusion model (LSR-Diff), which incorporates a diffusion model with a hybrid mask representation to better capture boundary information, and a novel strategy <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>nsemble <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula>ggregation via Level <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>et <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>volution (EASE) to merge prediction candidates while respecting structural information. The hybrid representation takes advantage of both discrete binary masks and continuous implicit masks, with an intermediate representation to ensure a smooth transition. The EASE module guided by ambiguity estimation and anatomical structure then refines boundary topology, preventing arbitrary mask assembly during the aggregation of stochastic predictions. We conduct extensive experiments across various clinical applications including multiple modalities and tissues, showing that LSR-Diff achieves competitive overall performance and improved edge quality and topology accuracy on the tested tasks.

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