The results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology workflows.
Abstract
Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning. We present a reproducible framework for evaluating uncertainty-aware segmentation under con- trolled clinical degradation. Our experiments use a synthetic multimodal brain tumor MRI cohort generated with a biophysical phantom simulator that follows the BraTS protocol. We train U-Net and Attention U-Net baselines for multi-class tumor sub-region segmentation and augment both models with Monte Carlo dropout to estimate per-voxel uncertainty. Across eight clinically motivated corruption types at five severity levels, we measure segmentation accuracy, calibration, failure detection, and selective prediction coverage. On clean data, Attention U-Net achieves a whole-tumor Dice of 0.990; under severe Gaussian noise, its performance falls to 0.089. Predictive uncertainty rises with degradation and tracks segmentation error (Pearson r = 0.53 under severity-3 Gaussian noise), allowing us to flag failures with an AUROC of 0.843. These results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology workflows. We release the code, trained models, and evaluation protocol to support direct reproduction.
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
Medical image segmentation is crucial for clinical diagnosis and treatment planning. However, in complex scenarios, factors such as boundary ambiguity, annotation noise, and domain shift often lead models to be overconfident, limiting the interpretability and trustworthy deployability. To address this, we propose a "plug-and-play" evidence-guided framework for reliable medical image segmentation. By incorporating an evidential constraint to suppress "high-confidence but erroneous" predictions, and without altering the existing segmentation backbone architecture, we append a lightweight evidential prediction head to enable the model to simultaneously output segmentation results and pixel-wise uncertainty in a single forward pass. An error-driven uncertainty calibration term is further introduced to enhance the joint calibration of evidence strength and scale, improving the stability, interpretability, and consistency of the risk indicator with the true error. Moreover, to reduce the reliance on manual empirical thresholds when utilizing uncertainty, we design an adaptive threshold selection process based on a validation set. During deployment, this enables the output of a reference-free high-risk region prompt for quality control, and, when annotations are available, facilitates quantitative validation through geometric consistency metrics between the uncertain region and the actual error region. We systematically evaluate our framework on a representative backbone, U-Net, across multiple datasets, and further investigate its architectural applicability through cross-backbone experiments. The results demonstrate that, with negligible increase in inference cost, our method generally achieves a more consistent, spatially coherent uncertainty representation that better aligns with the true error, providing a practical tool for quality control and risk awareness in clinical deployment. The source code is available at: https://github.com/Xingchao003/EviRisk-Seg.
Chaojie Xing, Haolin Zhan, Rencheng Song et al.· Medical Image Analysis· 0 citations
Medical image segmentation plays a critical role in computer-aided diagnosis by enabling precise delineation of abnormalities in medical images. Accurate segmentation of these images is essential for early diagnosis and treatment planning. However, conventional models such as U-Net face challenges including boundary uncertainty, variations in image quality and limited generalization across datasets.
This study presents MRD-UNet, a U-Net based segmentation framework built on a channel-disjoint multi-path dilated fusion (MDF) block deployed uniformly across all encoder and decoder stages, a residual formulation within each MDF block that stabilizes gradient flow without introducing pooling branches, and a task-driven distribution of three functionally distinct attention modules. We evaluate the proposed model through extensive experiments on the CVC-ClinicDB dataset comprising 612 endoscopic images and the ISIC-2018 dataset containing 2,594 dermoscopic images. During data augmentation, we apply geometric transformations, elastic deformations, and brightness adjustments; training is performed using a composite loss that combines Dice loss and binary cross-entropy.
The proposed method achieves a DSC of 0.9140 on CVC-ClinicDB and 0.9106 on ISIC-2018, yielding an improvement of approximately 2–3% over the baseline U-Net architecture. Ablation studies confirm that the channel-disjoint MDF design reduces the number of parameters relative to the baseline while improving sensitivity to small and low-contrast lesions, and that the targeted attention layout enhances boundary delineation.
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency. It outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters. Cross-dataset evaluations demonstrate its ability to generalize within the same imaging modality. Notably, the lightweight MRD-UNet-S variant maintains competitive accuracy, showing the architecture’s suitability for resource-constrained medical image segmentation tasks.
Not applicable.
Musa Doğan, I. Ozkan· BMC Medical 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
A Dynamic Uncertainty-aware Network (DynU-Net) is proposed, a multi-task framework that adaptively balances segmentation and classification through learnable per-task uncertainty parameters that consistently outperforms both single-task and existing multi-task baselines.
Ngoc Ly Tran, Thi Thu Thuy Nguyen, Ba-Hung Ngo et al.· Journal of Computational Des...· 0 citations
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.
Na Zeng, Qiao Lin, Xingyue Wang et al.· IEEE Transactions on Medical...· 0 citations