Artery extraction from X-ray coronary angiography (XCA) images is essential for the accurate diagnosis and treatment of coronary artery diseases. However, vessel visibility is significantly obscured by superimposed fluoroscopic densities from bones and soft tissues. Traditional digital subtraction angiography techniques are ineffective due to severe image degradation caused by cardiac motion. The development of deep learning-based methods has been hindered by the lack of large-scale datasets with high-quality annotations. To address this challenge, recent studies have explored training-free vessel extraction models, but their non-data-driven nature limits robustness in handling complex real-world data. In this work, we propose a novel framework that leverages synthetic temporal XCA data to a train deep learning model without the need for human annotation. First, we develop a comprehensive pipeline to synthesize large-scale, realistic temporal XCA data with anatomical variability and realistic artifacts simulation. Second, we introduce a DOmain-Shuffle Temporal Attention Network (DOSTA-Net), which enhances temporal feature learning by shuffling synthetic and real data along the temporal channel, effectively utilizing temporal information while mitigating domain discrepancies. Third, we generate the pseudo-label for real data and employ an annealing loss function to further reduce the domain gap between real and synthetic data to better utilize the unlabeled real data. The proposed method is evaluated based on the vessel segmentation performance on two datasets using the extracted arteries. Additionally, we conduct a reader study on an in-house real XCA dataset through subjective image quality assessment. Experimental results demonstrate that our approach outperforms state-of-the-art methods. Code and trained model weights are available at https://github.com/Advanced-AI-in-Medicine-and-Physics-Lab/DOSTA-Net.

Alzheimer’s disease (AD) poses a significant global challenge, with a notable absence of accessible and cost-effective diagnostic tools for widespread AD detection. The retina, mirroring the brain in anatomy and physiology, has emerged as a potential avenue for rapid AD identification through retinal imaging. The current retinal image-based AD detection methods usually focus primarily on the macular area, but ignore the potential value that the optic disc region may have for the detection task. In this study, we leverage both macular- and disc-centered OCTA images and propose a multi-region fusion framework for AD detection. Based on clinical evidence, we integrate handcrafted features into the framework to improve model performance and interpretability. Specifically, vascular morphological parameters extracted from the macular and disc regions are used as input to a revalued KNN model to improve predictive capabilities. Furthermore, recognizing the significance of extracting and utilizing complementary information from the macular and optic disc regions, we propose an uncertainty-guided strategy based on Dempster-Shefer Theory (DST) to fuse knowledge from different regions. This approach considers each region’s forecast quality and significantly improves the effectiveness and robustness of the model. Through comparative analysis with existing methods, we have demonstrated that our method outperforms the state-of-the-art ones and provides more valuable pathological evidence for the association between retinal vascular changes and AD.

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different en face angiograms from various retinal layers, rather than following existing methods that use only a single en face. We carry out extensive experiments on three OCTA datasets acquired using different imaging devices, and the results demonstrate that the proposed method performs on the whole better than either the state-of-the-art single-purpose methods or existing multi-task learning solutions. We also demonstrate that our multi-task learning method generalizes across other imaging modalities, such as color fundus photography, and may potentially be used as a general multi-task learning tool. We also construct three datasets for multiple structure detection, and part of these datasets with the source code and evaluation benchmark have been released for public access.