The Major Temporal Arcade (MTA) is a critical component of the retinal structure that facilitates clinical diagnosis and monitoring of various ocular pathologies. Although recent works have addressed the quantitative analysis of the MTA through parametric modeling, their efforts are strongly based on an assumption of symmetry in the MTA shape. This work presents a robust method for the detection and piecewise parametric modeling of the MTA in fundus images. The model consists of a piecewise parametric curve with the ability to consider both symmetric and asymmetric scenarios. In an initial stage, multiple models are built from random blood vessel points taken from the blood-vessel segmented retinal image, following a weighted-RANSAC strategy. To choose the final model, the algorithm extracts blood-vessel width and grayscale-intensity features and merges them to obtain a coarse MTA probability function, which is used to weight the percentage of inlier points for each model. This procedure promotes selecting a model based on points with high MTA probability. Experimental results in the public benchmark dataset Digital Retinal Images for Vessel Extraction (DRIVE), for which manual MTA delineations have been prepared, indicate that the proposed method outperforms existing approaches with a balanced Accuracy of $0.7067$, Mean Distance to Closest Point of $7.40$ pixels, and Hausdorff Distance of $27.96$ pixels, while demonstrating competitive results in terms of execution time ($9.93$ s per image).

Blood vessel segmentation in X-ray coronary angiography (XCA) plays a crucial role in diagnosing cardiovascular diseases, enabling a precise assessment of arterial structures. However, segmentation is challenging due to a low signal-to-noise ratio, interfering background structures, and vessel bifurcations, which hinder the accuracy of deep learning models. Additionally, deep learning models for this task often require high computational resources, limiting their practical application in real-time clinical settings. This study proposes a lightweight variant of the U-Net architecture using a structured kernel pruning strategy inspired by the Lottery Ticket Hypothesis. The pruning method systematically removes entire convolutional filters from each layer based on a global reduction factor, generating compact subnetworks that retain key representational capacity. This results in a significantly smaller model without compromising the segmentation performance. This approach is evaluated on two benchmark datasets, demonstrating consistent improvements in segmentation accuracy compared to the vanilla U-Net. Additionally, model complexity is significantly reduced from 31 M to 1.9 M parameters, improving efficiency while maintaining high segmentation quality.

Coronary stenosis results from unnatural narrowing of the heart arteries due to the accumulation of adipose depots, leading to different heart diseases and yielding top mortality worldwide. Thus far, deep learning-based methods for automatic stenosis over X-ray Coronary Angiography (XCA) have employed state-of-the-art architectures to solve the ImageNet challenge. With the advance of deep learning, contemporary architectures incorporated a variety of attention mechanisms to improve performance. Therefore, this paper presents a study of three attention mechanisms for stenosis detection in XCA images. Extensive experiments and comparisons over different Residual backbone networks are presented to verify the effectiveness of including such attention modules. An improvement of 4%, 10%, and 10% on the accuracy, recall, and F1-score was achieved using the approach, reaching mean values of 0.8787, 0.8610, and 0.8732, respectively.