Objectives: Spontaneous cerebrospinal fluid (CSF) leaks typically present in patients with undiagnosed idiopathic intracranial hypertension (IIH) secondary to pressure erosion of the skull base. Despite elevated intracranial pressure (ICP) on lumbar puncture or ventriculostomy, patients with spontaneous CSF leaks rarely complain of visual disturbances. The objective of this study is to correlate the presence of preoperative papilledema with opening ICP in patients undergoing endoscopic repair of spontaneous CSF leaks. Methods: Prospective evaluation of patients with spontaneous CSF leaks was performed over a 1-year period (December 2012 to December 2013). Fundoscopic examination for papilledema was completed preoperatively and CSF pressure was measured by lumbar puncture or ventriculostomy intraoperatively. Data regarding demographics, nature of presentation, and body mass index (BMI) were also recorded and compared to a control cohort of IIH patients with papilledema. Results: Sixteen patients (avg. age 52 years) were evaluated. Obesity was present in 94% of individuals (avg. BMI 43.5; range, 27-65). Papilledema was absent preoperatively in all subjects. Opening pressures via lumbar puncture/ventriculostomy were 27 ± 7.7 cmH20. Following 6 hours of clamping, measurements significantly increased to 36 ± 9.7 cmH20 ( P < .001). IIH controls (avg. age 33 years, avg. BMI 36.7, 22-52) exhibited average ICP (36 ± 10.5) identical to post-clamp measurements in the spontaneous CSF leak cohort. Conclusions: Subjects with spontaneous CSF leaks had post-clamping average ICP identical to controls with IIH and papilledema. Such evidence suggests that a CSF leak in this patient population provides sufficient pressure diversion to avoid the development of papilledema.

Seafloor multiparametric fibre-optic-cabled video observatories are emerging tools for standardized monitoring programmes, dedicated to the production of real-time fishery-independent stock assessment data. Here, we propose that a network of cabled cameras can be set up and optimized to ensure representative long-term monitoring of target commercial species and their surrounding habitats. We highlight the importance of adding the spatial dimension to fixed-point-cabled monitoring networks, and the need for close integration with Artificial Intelligence pipelines, that are necessary for fast and reliable biological data processing. We then describe two pilot studies, exemplary of using video imagery and environmental monitoring to derive robust data as a foundation for future ecosystem-based fish-stock and biodiversity management. The first example is from the NE Pacific Ocean where the deep-water sablefish (Anoplopoma fimbria) has been monitored since 2010 by the NEPTUNE cabled observatory operated by Ocean Networks Canada. The second example is from the NE Atlantic Ocean where the Norway lobster (Nephrops norvegicus) is being monitored using the SmartBay observatory developed for the European Multidisciplinary Seafloor and water column Observatories. Drawing from these two examples, we provide insights into the technological challenges and future steps required to develop full-scale fishery-independent stock assessments.

Underwater Television (UWTV) surveys provide fishery-independent stock size estimations of the Norway lobster (Nephrops norvegicus), based directly on burrow counting using the survey assumption of “one animal = one burrow”. However, stock size may be uncertain depending on true rates of burrow occupation. For the first time, 3055 video transects carried out in several Functional Units (FUs) around Ireland were used to investigate this uncertainty. This paper deals with the discrimination of burrow emergence and door-keeping diel behaviour in Nephrops norvegicus, which is one of the most commercially important fisheries in Europe. Comparisons of burrow densities with densities of visible animals engaged in door-keeping (i.e. animals waiting at the tunnel entrance) behaviour and animals in full emergence, were analysed at time windows of expected maximum population emergence. Timing of maximum emergence was determined using wave-form analysis and GAM modelling. The results showed an average level of 1 visible Nephrops individual per 10 burrow systems, depending on sampling time and depth. This calls into question the current burrow occupancy assumption which may not hold true in all FUs. This is discussed in relation to limitations of sampling methodologies and new autonomous robotic technological solutions for monitoring.

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The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) and Marine Institute Ireland (MI-Ireland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine experts. This is quite a time-consuming job. As a solution, we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision. The proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops burrows. This research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and classification. We conduct experiments on two data sets, namely, the Smalls Nephrops survey (FU 22) and Cadiz Nephrops survey (FU 30), collected by Marine Institute Ireland and Spanish Oceanographic Institute, respectively. From the results, we observe that the Inception model achieved a higher precision and recall rate than the MobileNet model. The best mean Average Precision (mAP) recorded by the Inception model is 81.61% compared to MobileNet, which achieves the best mAP of 75.12%.

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This paper proposes an algorithm for mosaicing videos generated during stock assessment of seabed-burrowing species. In these surveys, video transects of the seabed are captured and the population is estimated by counting the number of burrows in the video. The mosaicing algorithm is designed to process a large amount of video data and summarize the relevant features for the survey in a single image. Hence, the algorithm is designed to be computationally inexpensive while maintaining a high degree of robustness. We adopt a registration algorithm that employs a simple translational motion model and generates a mapping to the mosaic coordinate system using a concatenation of frame-by-frame homographies. A temporal smoothness prior is used in a maximum a posteriori homography estimation algorithm to reduce noise in the motion parameters in images with small amounts of texture detail. A multiband blending scheme renders the mosaic and is optimized for the application requirements. Tests on a large data set show that the algorithm is robust enough to allow the use of mosaics as a medium for burrow counting. This will increase the verifiability of the stock assessments as well as generate a ground truth data set for the learning of an automated burrow counting algorithm.