Introduction of image-based water transparency descriptors to quantify marine snow and turbidity features. A study with data from a stationary observatory

Möller T, Nilssen I, Nattkemper TW. Introduction of image-based water transparency descriptors to quantify marine snow and turbidity features. A study with data from a stationary observatory. Presented at the MIW 2014 - Marine Imaging Workshop, Southampton

A data science approach for multi-sensor marine observatory data monitoring cold water corals (Paragorgia arborea) in two campaigns

Fixed underwater observatories (FUO), equipped with digital cameras and other sensors, become more commonly used to record different kinds of time series data for marine habitat monitoring. With increasing numbers of campaigns, numbers of sensors and campaign time, the volume and heterogeneity of the data, ranging from simple temperature time series to series of HD images or video call for new data science approaches to analyze the data. While some works have been published on the analysis of data from one campaign, we address the problem of analyzing time series data from two consecutive monitoring campaigns (starting late 2017 and late 2018) in the same habitat. While the data from campaigns in two separate years provide an interesting basis for marine biology research, it also presents new data science challenges, like the the marine image analysis in data form more than one campaign. In this paper, we analyze the polyp activity of two Paragorgia arborea cold water coral (CWC) colonies using FUO data collected from November 2017 to June 2018 and from December 2018 to April 2019. We successfully apply convolutional neural networks (CNN) for the segmentation and classification of the coral and the polyp activities. The result polyp activity data alone showed interesting temporal patterns with differences and similarities between the two time periods. A one month “sleeping” period in spring with almost no activity was observed in both coral colonies, but with a shift of approximately one month. A time series prediction experiment allowed us to predict the polyp activity from the non-image sensor data using recurrent neural networks (RNN). The results pave a way to a new multi-sensor monitoring strategy for Paragorgia arborea behaviour.publishedVersio

Computational analysis of spatial species distribution for integrated stationary environmental monitoring

Osterloff J, Nilssen I, Möller T, Nattkemper TW. Computational analysis of spatial species distribution for integrated stationary environmental monitoring. Presented at the Geohab 2015, Salvador, Bahia, Brazil

Extracting Scalar Quantities from Underwater Images - a Toolbox for Image Data from Fixed Observatories.

Osterloff J, Möller T, Nilssen I, Nattkemper TW. Extracting Scalar Quantities from Underwater Images - a Toolbox for Image Data from Fixed Observatories. Presented at the Marine Imaging Workshop 2017, Kiel

Change Detection in underwater time laps videos from stationary observatories

Möller T, Nilssen I, Sumida P, Osterloff J, Nattkemper TW. Change Detection in underwater time laps videos from stationary observatories. Presented at the GEOHAB, Salvador, Brazil

The Role of Psychological Readiness in Return to Sport Assessment After Anterior Cruciate Ligament Reconstruction

Background: Knowledge about the predictive value of return to sport (RTS) test batteries applied after anterior cruciate ligament reconstruction (ACLR) is limited. Adding assessment of psychological readiness has been recommended, but knowledge of how this affects the predictive ability of test batteries is lacking. Purpose: To examine the predictive ability of a RTS test battery on return to preinjury level of sport and reinjury when evaluation of psychological readiness was incorporated. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 129 patients were recruited 9 months after ACLR. Inclusion criteria were age ≥16 years and engagement in sports before injury. Patients with concomitant ligamentous surgery or ACL revision surgery were excluded. Baseline testing included single-leg hop tests, isokinetic strength tests, the International Knee Documentation Committee (IKDC) Subjective Knee Form 2000, a custom-made RTS questionnaire, and the Anterior Cruciate Ligament-Return to Sport after Injury (ACL-RSI) scale. The RTS criteria were IKDC 2000 score ≥85% and ≥85% leg symmetry index on hop and strength test. At a 2-year follow-up evaluation, further knee surgery and reinjuries were registered and the RTS questionnaire was completed again. Regression analyses and receiver operating characteristic analyses were performed to study the predictive ability of the test battery. Results: Out of the 103 patients who completed the 2-year follow-up, 42% returned to their preinjury level of sport. ACL-RSI 9 months after surgery (odds ratio [OR], 1.03) and age (OR, 1.05) predicted RTS. An ACL-RSI score <47 indicated that a patient was at risk of not returning to sport (area under the curve 0.69; 95% CI, 0.58-0.79), with 85% sensitivity and 45% specificity. The functional tests did not predict RTS. Six patients sustained ACL reinjuries and 7 underwent surgery for other knee complaints/injuries after RTS testing. None of the 29 patients who passed all RTS criteria, and were therefore cleared for RTS, sustained a second knee injury. Conclusion: ACL-RSI and age were predictors of 2-year RTS, while functional tests were not informative. Another main finding was that none of the patients who passed the 85% RTS criteria sustained another knee injury.publishedVersio

Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory.

Osterloff J, Nilssen I, Jarnegren J, Van Engeland T, Buhl-Mortensen P, Nattkemper TW. Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory. Scientific reports. 2019;9(1): 6578.An array of sensors, including an HD camera mounted on a Fixed Underwater Observatory (FUO) were used to monitor a cold-water coral (Lophelia pertusa) reef in the Lofoten-Vesteralen area from April to November 2015. Image processing and deep learning enabled extraction of time series describing changes in coral colour and polyp activity (feeding). The image data was analysed together with data from the other sensors from the same period, to provide new insights into the short- and long-term dynamics in polyp features. The results indicate that diurnal variations and tidal current influenced polyp activity, by controlling the food supply. On a longer time-scale, the coral's tissue colour changed from white in the spring to slightly red during the summer months, which can be explained by a seasonal change in food supply. Our work shows, that using an effective integrative computational approach, the image time series is a new and rich source of information to understand and monitor the dynamics in underwater environments due to the high temporal resolution and coverage enabled with FUOs

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

Osterloff J, Nilssen I, Eide I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Nattkemper TW. Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE. 2016;11(6): e0157329.This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors

Automated Image based Biomass Quantification in Mesocosm Studies

Osterloff J, Nilssen I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Möller T, Nattkemper TW. Automated Image based Biomass Quantification in Mesocosm Studies. Presented at the Geohab 2014, Lorne, Victoria, Australia

Change Detection in Marine Observatory Image Streams using Bi-Domain Feature Clustering

Möller T, Nilssen I, Nattkemper TW. Change Detection in Marine Observatory Image Streams using Bi-Domain Feature Clustering. In: 2016 23rd International Conference on Pattern Recognition (ICPR). Piscataway, NJ: IEEE; 2016: 793-798