Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey

Exploring the Connection between Uncertainty and Tissue Boundaries in Medical Image Segmentation.

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.

Medical image segmentation with optimal learning from limited data and annotations: A comprehensive review

MRD-UNet: a compute-efficient medical image segmentation architecture with multi-attention and parallel dilated fusion

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.

Semi-supervised Medical Image Segmentation via Perturbation-Aware Mutual Learning and Edge-Aware Uncertainty Loss for Accurate Anatomical Delineation.

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.

Uncertainty-guided mamba network for efficient medical image segmentation with evidential deep learning.

This work proposes an uncertainty-aware efficient segmentation framework synergizing Mamba state-space models with evidential deep learning, employing a 2D-adapted selective state-space mechanism to capture long-range dependencies with linear complexity O(L), overcoming transformers' quadratic scaling.

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

The results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology workflows.