Image segmentation plays a key role in many image-guided clinical applications and deep learning technology has proven effective for this task when sufficient labeled images are available. However, it is very time-consuming and labor-intensive to obtain adequate pixel-level image labels. To alleviate the scarcity of labeled images, we propose a novel semi-supervised segmentation method based on the available uncertainty-aware mean teacher (UAMT) framework by introducing two different strategies, i.e., selective self-ensembling (SSE) and boundary uncertainty suppression (BUS). The SSE dynamically selects multiple best student models across different training steps to update the teacher model's weights, while the BUS reduces boundary segmentation errors and improves the quantitative potential of loss functions through a unique uncertainty estimation function. With the two strategies, our proposed method was able to obtain promising segmentation performance with limited labeled images and abundant unlabeled ones. We trained and validated our proposed method by segmenting multiple objects from three public datasets (i.e., PROMISE, REFUGE, and RETA). Extensive experiments showed that our proposed method achieved better segmentation performance than the UAMT, along with the average Dice score (DSC) of 0.7990 for three different objects, and can compete with several existing semi-supervised methods (i.e., HCMT, SASSNet, and DTC). • A novel semi-supervised learning method was developed for accurate image segmentation. • A performance-driven exponential moving average (pEMA) was proposed for semi-supervised learning. • A unique exponential function was proposed for uncertainty estimation. • Extensive segmentation experiments showed the advantage of the developed method.

Accurate segmentation of medical images is essential for various image processing tasks and is now predominantly achieved using deep learning techniques. However, existing approaches often employ loss functions that fail to account for pixel-level differences in prediction uncertainty or hardness. This limitation frequently results in relatively large segmentation errors, particularly in object boundary regions. To address the limitation, we developed a novel class of uncertainty- / hardness-weighted loss functions by introducing two distinct pixel-wise weighting schemes: probability-guided uncertainty (PGU) and region-enhanced hardness (REH) weights. These weights, derived from the differences between network predictions and their corresponding ground truths, were designed to emphasize challenging pixels while reducing segmentation uncertainties. We validated these loss functions by integrating them with two classical neural networks, i.e., Swin Transformer based U-shape network (Swin-Unet) and V-shape network (V-Net) to segment two- and three-dimensional target objects across four different images datasets, including Retinal Fundus Glaucoma Challenge (REFUGE) dataset, Retinal Vascular Tree Analysis (RETA) dataset, optical coherence tomography (OCT) dataset, and Atria Segmentation Challenge (ASC) dataset. Extensive experiments demonstrated that our developed loss functions outperformed classical losses, such as cross-entropy (CE) and Dice losses, along with their variants, highlighting the effectiveness and generalization of the introduced weighting schemes. The source code is available at https://github.com/wmuLei/uhLoss.

High-resolution Optical Coherence Tomography Angiography (OCTA) images are essential for morphological analysis and biomarker measurement of the retinal vasculature. They can also provide underlying biomarkers for the accurate analysis of eye-related diseases. The trade-off between the high resolution (HR) and large scanning field-of-view (FOV) is a long-standing problem for OCTA image instrument. A large FOV image provides more retinal information with shorter acquisition time but often suffers from low resolution (LR), high scatter noise, and poor vascular contrast. In order to obtain HR OCTA images with larger FOV, we propose a novel self-similar dynamic domain adaptation network based on cross-field-of-view representation learning. The network enables LR images (i.e., $6\times \text{6}\,\text{mm}^{2}$) to learn HR image (i.e., $3\times \text{3}\,\text{mm}^{2}$) feature representations specialized for OCTA by constructing feature mapping relations for cross-field-of-view OCTA scans. To be specific, a multiple random degradation model is proposed on HR images to generate various synthetic LR images. Further, we propose a dynamic domain adaptation framework that prompts feature dynamic alignment of the LR image reconstruction results with those of synthetic LR images. Finally, a novel self-similar supervision loss is proposed to optimize the reconstruction results from LR to HR by exploiting the similarity between vessels in different regions. Experimental results on three OCTA datasets show that the proposed method surpasses existing state-of-the-art ones, significantly enhancing retinal structure segmentation and disease classification. Our OCTA dataset (the first dataset in this research area with paired $3\times 3$ and $6\times \text{6}\,\text{mm}^{2}$ OCTA images) and code are publicly available.

Choroidal neovascularization (CNV), a primary characteristic of wet age-related macular degeneration (wet AMD), represents a leading cause of blindness worldwide. In clinical practice, optical coherence tomography angiography (OCTA) is commonly used for studying CNV-related pathological changes, due to its micron-level resolution and non-invasive nature. Thus, accurate segmentation of CNV regions and vessels in OCTA images is crucial for clinical assessment of wet AMD. However, challenges existed due to irregular CNV shapes and imaging limitations like projection artifacts, noises and boundary blurring. Moreover, the lack of publicly available datasets constraints the CNV analysis. To address these challenges, this paper constructs the first publicly accessible CNV dataset (CNVSeg), and proposes a novel multilateral graph convolutional interaction-enhanced CNV segmentation network (MTG-Net). This network integrates both region and vessel morphological information, exploring semantic and geometric duality constraints within the graph domain. Specifically, MTG-Net consists of a multi-task framework and two graph-based cross-task modules: Multilateral Interaction Graph Reasoning (MIGR) and Multilateral Reinforcement Graph Reasoning (MRGR). The multi-task framework encodes rich geometric features of lesion shapes and surfaces, decoupling the image into three task-specific feature maps. MIGR and MRGR iteratively reason about higher-order relationships across tasks through a graph mechanism, enabling complementary optimization for task-specific objectives. Additionally, an uncertainty-weighted loss is proposed to mitigate the impact of artifacts and noise on segmentation accuracy. Experimental results demonstrate that MTG-Net outperforms existing methods, achieving a Dice socre of 87.21% for region segmentation and 88.12% for vessel segmentation.