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

Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation

In practical settings, medical image segmentation models are often developed with limited annotated data rather than fully labeled datasets. Training frequently begins in ultra-low labeled regimes where only a small number of volumes are annotated. In such scenarios, practitioners must simultaneously decide which cases to annotate and how to best use the remaining unlabeled data. Although active learning (AL) and semi-supervised learning (SSL) both target annotation scarcity, they are typically designed and optimized independently, resulting in objective mismatch and unstable training during early-stage"cold start"conditions. We propose RegAL, a unified active semi-supervised framework governed by a shared topology-aware Pareto optimization that couples sample acquisition with unlabeled data utilization. RegAL evaluates images along three complementary axes, voxel-wise uncertainty, feature diversity, and a novel topological consistency metric, to select anatomically informative edge cases for annotation. On the other hand, the same criteria are used to identify geometrically stable atlas candidates for diffeomorphic registration-guided augmentation to train a self-supervised Mean Teacher segmentation network. Across BraTS 2021, dHCP, and ProstateX, RegAL remains stable with few labeled volumes and consistently outperforms state-of-the-art AL, SSL, and active semi-supervised baselines across Dice and boundary-distance (ASD, HD95) metrics under extreme annotation scarcity.

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

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.

Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey

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.

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.

Bidirectional uncertainty-aware region learning for semi-supervised medical image segmentation.

In semi-supervised medical image segmentation, the poor quality of unlabeled data and the uncertainty in the model's predictions often lead to the generation of incorrect pseudo-labels by the model. These errors accumulate throughout model training, thereby weakening the model's performance. We found that these erroneous pseudo-labels are typically concentrated in high-uncertainty regions. Traditional methods improve performance by directly discarding pseudo-labels in these regions, which can also result in neglecting potentially valuable training data. To alleviate this problem, we propose a bidirectional uncertainty-aware region learning strategy to fully utilize the precise supervision provided by labeled data and stabilize the training of unlabeled data. Specifically, in the training labeled data, we focus on high-uncertainty regions, using precise label information to guide the model's learning in potentially uncontrollable areas. Meanwhile, in the training of unlabeled data, we concentrate on low-uncertainty regions to reduce the interference of erroneous pseudo-labels on the model. Through this bidirectional learning strategy, the model's overall performance has significantly improved. Extensive experiments show that our proposed method achieves significant performance improvement on different medical image segmentation tasks.