Abstract
This work presents an experimental framework for offshore seismic monitoring that combines Distributed Acoustic Sensing (DAS) with ocean-bottom seismometers (OBS). The study was conducted in the Azores region - Faial, where an HDAS interrogator prototype was connected to dark fiber submarine fiber-optic cable, complemented by the installation of two Ocean Bottom Seismometers (OBS) for calibration and validation of DAS technology. The main objective is to demonstrate that seismic observations obtained by DAS from seafloor cables can provide essential information similar to OBS and particularly in areas where land-based monitoring stations are limited.

Abstract
The segmentation of the foveal avascular zone (FAZ) in optical coherence tomography angiography (OCTA) images plays a crucial role in diagnosing and monitoring ocular diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). However, accurate FAZ segmentation remains challenging due to image quality and variability. This paper provides a comprehensive review of FAZ segmentation techniques, including traditional image processing methods and recent deep learning-based approaches. We propose two novel deep learning methodologies: a multitask learning framework that integrates vessel and FAZ segmentation, and a conditionally trained network that employs vessel-aware loss functions. The performance of the proposed methods was evaluated on the OCTA-500 dataset using the Dice coefficient, Jaccard index, 95% Hausdorff distance, and average symmetric surface distance. Experimental results demonstrate that the multitask segmentation framework outperforms existing state-of-the-art methods, achieving superior FAZ boundary delineation and segmentation accuracy. The conditionally trained network also improves upon standard U-Net-based approaches but exhibits limitations in refining the FAZ contours.

Abstract

Abstract

Abstract

Abstract