Aug 2026· IEEE Transactions on Medical Imaging· Vol PP, pp. 1-1· 0 citations
Medicine
TL;DR
An Evidential Uncertainty-Guided Boundary (EUGB) loss is proposed to demonstrate that uncertainty information can indeed facilitate combating boundary segmentation errors, and empirical insights for selecting appropriate loss functions across different application scenarios are provided.
Abstract
Automatic medical image segmentation, as a prerequisite for clinical quantitative analysis, forms the basis of computer-aided diagnosis. However, blurry object boundaries caused by factors such as imaging quality and inherent physiological properties of tissues or lesions are the main causes of imprecise segmentation. This aligns with the common understanding that high uncertainty and misclassification tend to occur at boundaries in segmentation. To address the challenge, we investigate this phenomenon and explore the connection between uncertainty and tissue boundaries by analysing various tissues. Then an Evidential Uncertainty-Guided Boundary (EUGB) loss is further proposed to demonstrate that uncertainty information can indeed facilitate combating boundary segmentation errors. The proposed EUGB loss not only emphasizes challenging pixels along blurry boundaries using evidential uncertainty, but also introduces a regularization term that constrains uncertainty learning by penalizing incorrect predictions and reinforcing correct ones. The effectiveness of the proposed EUGB loss is verified in the public LIDC-IDRI, ISIC 2018, and OCTA-500 datasets with two classic medical image segmentation networks (U-Net and TransU-Net). Experimental results demonstrate that the proposed loss outperforms seven other segmentation loss functions in terms of boundary segmentation, while maintaining competitive region-level segmentation accuracy. Beyond introducing a new loss function, this paper provides empirical insights for selecting appropriate loss functions across different application scenarios. We systematically analyze the strengths and limitations of existing losses from multiple perspectives, including reliability and dataset characteristics. This analysis offers practical insights that enable researchers and practitioners to optimize segmentation performance based on specific data attributes.
A comprehensive survey of UQ techniques in medical image segmentation is presented, categorizing existing approaches into Bayesian methods, deep ensembles, deterministic methods, test-time data augmentation, and hybrid models, while treating foundation-model-based UQ as a separate cross-cutting category.
Seyed Sina Ziaee, K. Ovens· Journal of Imaging· 0 citations
This paper proposes a novel framework that effectively leverages unlabeled data to improve segmentation performance in cardiac structures and applies a novel consistency constraint by a dual fine-grained boundary loss that provide global characteristics-based guidance from the transition of the boundary region and an edge-aware uncertainty loss.
Waqas Anwaar, Van Manh, Wufeng Xue et al.· Interdisciplinary Sciences C...· 0 citations
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Musa Doğan, I. Ozkan· BMC Medical Imaging· 0 citations
Medical ultrasound (US) image segmentation faces significant challenges due to speckle noise, low-contrast boundaries, acoustic shadowing, and acquisition variation across operators and clinical centers. Although encoder-decoder and transformer-based networks have achieved strong performance, many methods recover boundary details through dense decoders or larger backbones, which may still produce over-smoothed contours or unstable predictions under external distribution shifts. In this article, we propose Risk-routed Implicit Boundary Refinement (RIBR), a compact segmentation framework that uses implicit neural representation as a risk-routed residual correction rather than an unconstrained full-mask predictor. RIBR combines boundary-refinement implicit residuals, risk-routed residual control, and geometry- and speckle-aware boundary regularization to refine uncertain contours while suppressing non-boundary oscillations. Evaluation on nine US datasets covering lymph nodes, breast lesions, thyroid nodules, and prostate shows that RIBR achieves the best overall macro-average and consistently reduces boundary error across grouped and organ-specific comparisons under a compact parameter budget. These findings suggest that controlled implicit residual learning is a practical strategy for resource-constrained and boundary-sensitive US segmentation. Source code is available at https://github.com/jinggqu/ribr.
Jingguo Qu, Xinyang Han, Xiang Wang et al.· 0 citations
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.
Wenbo Gao, Haoyu Cao, J. Cheung et al.· IEEE Transactions on Image P...· 0 citations
Medical image segmentation plays an important role in clinical imaging for disease diagnosis, treatment planning, and follow-up analysis. Zero-shot segmentation models, like segment anything model are able to perform well but ultrasound images remain challenging due to speckle noise, low contrast and poor anatomical boundaries. Furthermore, when these models generate multiple masks per image, identifying which mask provides the best segmentation results can be difficult. The candidate mask selection problem in zero-shot medical image segmentation was considered limited detail by previous research. Therefore, this paper presents a lightweight arbitration framework that identifies clinically relevant masks from multiple candidates using a standard segmentation model, without retraining or modifying the model. The approach evaluates candidate masks using soft anatomical and geometric constraints such as region size, structural compactness, and boundary interaction. Experiments were conducted on the BUSI breast ultrasound dataset under strict zero-shot conditions without supervised fine-tuning. Results indicate that rigid anatomical assumptions reduce segmentation reliability in highly variable ultrasound images, while softer geometric constraints improve robustness during mask selection. The findings further show that boundary-aware anatomical reasoning provides more consistent candidate selection than relying only on global spatial priors.
Trishita Acharjee, R. China, Appala Naidu· 2026 4th International Confe...· 0 citations