3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay

This study presents a novel approach to high-resolution density distribution mapping of two key species of the 1170 "Reefs" habitat, Dendrophyllia cornigera and Phakellia ventilabrum, in the Bay of Biscay using deep learning models. The main objective of this study was to establish a pipeline based on deep learning models to extract species density data from raw images obtained by a remotely operated towed vehicle (ROTV). Different object detection models were evaluated and compared in various shelf zones at the head of submarine canyon systems using metrics such as precision, recall, and F1 score. The best-performing model, YOLOv8, was selected for generating density maps of the two species at a high spatial resolution. The study also generated synthetic images to augment the training data and assess the generalization capacity of the models. The proposed approach provides a cost-effective and non-invasive method for monitoring and assessing the status of these important reef-building species and their habitats. The results have important implications for the management and protection of the 1170 habitat in Spain and other marine ecosystems worldwide. These results highlight the potential of deep learning to improve efficiency and accuracy in monitoring vulnerable marine ecosystems, allowing informed decisions to be made that can have a positive impact on marine conservation.

Environmental Status and Geomorphological Characterisation of Seven Black Coral Forests on the Sardinian Continental Shelf (NW Mediterranean Sea)

Simple Summary

Black coral forests are three-dimensional components of the marine mesophotic benthic community that play a crucial role in the benthic–pelagic processes, enhancing substrate complexity and creating numerous ecological niches and biodiversity hotspots. The increase of natural and human pressures on these forests is decimating their sophisticated architecture, leading to habitat degradation and biodiversity loss. This study assessed the environmental status of seven black coral forests dwelling in the centre of the Mediterranean Sea using the Mesophotic Assemblages Conservation Status Index. Our results showed how site-specific ecological conditions associated with different geomorphological settings can determine the variability of the environmental status among these habitats. Overall, most of the black coral forests investigated showed a “high” and “good” status; however, in two sites, a degraded benthic community and a marked anthropogenic impact determined a “moderate” and “poor” environmental status, highlighting the fragility of these communities to anthropogenic stressors, even in an area of low urbanisation, such as a Sardinian island. The scenario obtained by this study, combined with a more complete understanding of the processes that drive benthic communities’ dynamics, would facilitate the evaluation of potential measures for the appropriate management of human activities and the general conservation of mesophotic coral forests.

Abstract

Marine animal forests are key mesophotic ecosystems that are under threat from increasing natural and human pressures. Despite the fact that various international agreements strive to preserve these fragile ecosystems, the environmental status of the majority of these animal-structured environments is unknown. Assessing their environmental status is the first step needed to monitor these essential habitats’ health over time and include them within conservation and protection frameworks, such as the Marine Strategy Framework Directive. Based on Multibeam data and ROV footage, we characterized the geomorphological setting and evaluated the environmental status of seven black coral forests in the centre of the Western Mediterranean Sea, using the Mesophotic Assemblages Conservation Status (MACS) Index. The presence of two antipatharians, Antipathella subpinnata and Leiopathes glaberrima, characterized the seven investigated sites, dwelling on rocky substrate characterized by different environmental drivers (i.e., depth, slope of the substrate, terrain ruggedness, topographic positioning index, and aspect). From the combined evaluation of the associated benthic community status and the anthropogenic impacts affecting it, a “high” and “good” environmental status was assessed for five out of the seven studied black forests, with only two forests classified as having a “moderate” and “poor” status, respectively. Overall, our study showed a site-specific variability of mesophotic black coral forest status, explained by different biological community structures and environmental conditions mainly associated with morphological and anthropogenic factors.

Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

Underwater image segmentation is useful for benthic habitat mapping and monitoring; however, manual annotation is time-consuming and tedious. We propose automated segmentation of benthic habitats using unsupervised semantic algorithms. Four such algorithms––Fast and Robust Fuzzy C-Means (FR), Superpixel-Based Fast Fuzzy C-Means (FF), Otsu clustering (OS), and K-means segmentation (KM)––were tested for accuracy for segmentation. Further, YCbCr and the Commission Internationale de l’Éclairage (CIE) LAB color spaces were evaluated to correct variations in image illumination and shadow effects. Benthic habitat field data from a geo-located high-resolution towed camera were used to evaluate proposed algorithms. The Shiraho study area, located off Ishigaki Island, Japan, was used, and six benthic habitats were classified. These categories were corals (Acropora and Porites), blue corals (Heliopora coerulea), brown algae, other algae, sediments, and seagrass (Thalassia hemprichii). Analysis showed that the K-means clustering algorithm yielded the highest overall accuracy. However, the differences between the KM and OS overall accuracies were statistically insignificant at the 5% level. Findings showed the importance of eliminating underwater illumination variations and outperformance of the red difference chrominance values (Cr) in the YCbCr color space for habitat segmentation. The proposed framework enhanced the automation of benthic habitat classification processes.

Towards a better understanding of annotation tools for medical imaging: a survey

Medical imaging refers to several different technologies that are used to view the human body to diagnose, monitor, or treat medical conditions. It requires significant expertise to efficiently and correctly interpret the images generated by each of these technologies, which among others include radiography, ultrasound, and magnetic resonance imaging. Deep learning and machine learning techniques provide different solutions for medical image interpretation including those associated with detection and diagnosis. Despite the huge success of deep learning algorithms in image analysis, training algorithms to reach human-level performance in these tasks depends on the availability of large amounts of high-quality training data, including high-quality annotations to serve as ground-truth. Different annotation tools have been developed to assist with the annotation process. In this survey, we present the currently available annotation tools for medical imaging, including descriptions of graphical user interfaces (GUI) and supporting instruments. The main contribution of this study is to provide an intensive review of the popular annotation tools and show their successful usage in annotating medical imaging dataset to guide researchers in this area.

Affordable and Faster Transradial Prosthetic Socket Production Using Photogrammetry and 3D Printing

This study aims to invent a new, low-cost, and faster method of prosthetic socket fabrication, especially in Indonesia. In this paper, the photogrammetry with the 3D printing method is introduced as the new applicative way for transradial prosthetic making. Photogrammetry is used to retrieve a 3D model of the amputated hand stump using a digital camera. A digital camera is used for photogrammetry technique and the resulting 3D model is printed using a circular 3D printer with Polylactic acid (PLA) material. The conventional casting socket fabrication method was also conducted in this study as a comparison. Both prosthetic sockets were analyzed for usability, and sectional area conformities to determine the size deviation using the image processing method. This study concludes that the manufacturing of transradial prosthetic sockets incorporating the photogrammetry technique reduces the total man-hour production. Based on the results, it can be implied that the photogrammetry technique is a more efficient and economical method compared to the conventional casting method. The 3D printed socket resulting from the photogrammetry method has a 5–19% area deviation to the casting socket but it is still preferable and adjustable for the transradial amputee when applied to the stump of the remaining hand.