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  • 1
    Online Resource
    Online Resource
    Automated Activity Estimation of the Cold-Water Coral Lophelia pertusa by Multispectral Imaging and Computational Pixel Classification
    American Meteorological Society ; 2021
    In:  Journal of Atmospheric and Oceanic Technology Vol. 38, No. 2 ( 2021-02), p. 141-154
    Details
    In: Journal of Atmospheric and Oceanic Technology, American Meteorological Society, Vol. 38, No. 2 ( 2021-02), p. 141-154
    Abstract: The cold-water coral Lophelia pertusa builds up bioherms that sustain high biodiversity in the deep ocean worldwide. Photographic monitoring of the polyp activity represents a helpful tool to characterize the health status of the corals and to assess anthropogenic impacts on the microhabitat. Discriminating active polyps from skeletons of white Lophelia pertusa is usually time consuming and error prone due to their similarity in color in common red–green–blue (RGB) camera footage. Acquisition of finer-resolved spectral information might increase the contrast between the segments of polyps and skeletons, and therefore could support automated classification and accurate activity estimation of polyps. For recording the needed footage, underwater multispectral imaging systems can be used, but they are often expensive and bulky. Here we present results of a new, lightweight, compact, and low-cost deep-sea tunable LED-based underwater multispectral imaging system (TuLUMIS) with eight spectral channels. A branch of healthy white Lophelia pertusa was observed under controlled conditions in a laboratory tank. Spectral reflectance signatures were extracted from pixels of polyps and skeletons of the observed coral. Results showed that the polyps can be better distinguished from the skeleton by analysis of the eight-dimensional spectral reflectance signatures compared to three-channel RGB data. During a 72-h monitoring of the coral with a half-hour temporal resolution in the laboratory, the polyp activity was estimated based on the results of the multispectral pixel classification using a support vector machine (SVM) approach. The computational estimated polyp activity was consistent with that of the manual annotation, which yielded a correlation coefficient of 0.957.
    Type of Medium: Online Resource
    ISSN: 0739-0572 , 1520-0426
    URL: Article
    DOI: 10.1175/JTECH-D-19-0139.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
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    detail.hit.zdb_id: 48441-6
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  • 2
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    Ecology of a polymetallic nodule occurrence gradient: Implications for deep‐sea mining
    Wiley ; 2019
    In:  Limnology and Oceanography Vol. 64, No. 5 ( 2019-09), p. 1883-1894
    Details
    In: Limnology and Oceanography, Wiley, Vol. 64, No. 5 ( 2019-09), p. 1883-1894
    Abstract: Abyssal polymetallic nodule fields constitute an unusual deep‐sea habitat. The mix of soft sediment and the hard substratum provided by nodules increases the complexity of these environments. Hard substrata typically support a very distinct fauna to that of seabed sediments, and its presence can play a major role in the structuring of benthic assemblages. We assessed the influence of seafloor nodule cover on the megabenthos of a marine conservation area (area of particular environmental interest 6) in the Clarion Clipperton Zone (3950–4250 m water depth) using extensive photographic surveys from an autonomous underwater vehicle. Variations in nodule cover (1–20%) appeared to exert statistically significant differences in faunal standing stocks, some biological diversity attributes, faunal composition, functional group composition, and the distribution of individual species. The standing stock of both the metazoan fauna and the giant protists (xenophyophores) doubled with a very modest initial increase in nodule cover (from 1% to 3%). Perhaps contrary to expectation, we detected little if any substantive variation in biological diversity along the nodule cover gradient. Faunal composition varied continuously along the nodule cover gradient. We discuss these results in the context of potential seabed‐mining operations and the associated sustainable management and conservation plans. We note in particular that successful conservation actions will likely require the preservation of areas comprising the full range of nodule cover and not just the low cover areas that are least attractive to mining.
    Type of Medium: Online Resource
    ISSN: 0024-3590 , 1939-5590
    URL: Issue
    URL: Article
    DOI: 10.1002/lno.v64.5
    DOI: 10.1002/lno.11157
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2033191-5
    detail.hit.zdb_id: 412737-7
    SSG: 12
    SSG: 14
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  • 3
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    Megafaunal variation in the abyssal landscape of the Clarion Clipperton Zone
    Elsevier BV ; 2019
    In:  Progress in Oceanography Vol. 170 ( 2019-01), p. 119-133
    Details
    In: Progress in Oceanography, Elsevier BV, Vol. 170 ( 2019-01), p. 119-133
    Type of Medium: Online Resource
    ISSN: 0079-6611
    URL: Article
    DOI: 10.1016/j.pocean.2018.11.003
    RVK:
    RB 10402
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 1497436-8
    detail.hit.zdb_id: 4062-9
    SSG: 21,3
    SSG: 14
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  • 4
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    Postoperative single-sequence (PoSSe) MRI: imaging work-up for CT-guided or endoscopic drainage indication of collections after hepatopancreaticobiliary surgery
    Springer Science and Business Media LLC ; 2021
    In:  Abdominal Radiology Vol. 46, No. 7 ( 2021-07), p. 3418-3427
    Details
    In: Abdominal Radiology, Springer Science and Business Media LLC, Vol. 46, No. 7 ( 2021-07), p. 3418-3427
    Abstract: Fluid collections due to anastomotic leakage are a common complication after hepatopancreaticobiliary (HPB) surgery and are usually treated with drainage. We conducted a study to evaluate imaging work-up with a postoperative single-sequence (PoSSe) MRI for the detection of collections and indication of drainage. Material and methods Forty-six patients who developed signs of leakage (fever, pain, laboratory findings) after HPB surgery were prospectively enrolled. Each patient was examined by abdominal sonography and our PoSSe MRI protocol (axial T2-weighted HASTE only). PoSSe MRI examination time (from entering to leaving the MR scanner room) was measured. Sonography and MRI were evaluated regarding the detection and localization of fluid collections. Each examination was classified for diagnostic sufficiency and an imaging-based recommendation if CT-guided or endoscopic drainage is reasonable or not was proposed. Imaging work-up was evaluated in terms of feasibility and the possibility of drainage indication. Results Sonography, as first-line modality, detected 21 focal fluid collections and allowed to decide about the need for drainage in 41% of patients. The average time in the scanning room for PoSSe MRI was 9:23 min [7:50–13:32 min]. PoSSe MRI detected 46 focal collections and allowed therapeutic decisions in all patients. Drainage was suggested based on PoSSe MRI in 25 patients (54%) and subsequently indicated and performed in 21 patients (100% sensitivity and 84% specificity). No patient needed further imaging to optimize the treatment. Conclusions The PoSSe MRI approach is feasible in the early and intermediate postoperative setting after HPB surgery and shows a higher detection rate than sonography. Imaging work-up regarding drainage of collections was successful in all patients and our proposed PoSSe MRI algorithm provides an alternative to the standard work-up.
    Type of Medium: Online Resource
    ISSN: 2366-004X , 2366-0058
    URL: Article
    DOI: 10.1007/s00261-021-02955-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2845742-0
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  • 5
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    Online Resource
    Corrigendum: BIIGLE 2.0 - Browsing and Annotating Large Marine Image Collections
    Frontiers Media SA ; 2020
    In:  Frontiers in Marine Science Vol. 7 ( 2020-10-29)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 7 ( 2020-10-29)
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2020.617268
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2757748-X
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  • 6
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    DeepSurveyCam—A Deep Ocean Optical Mapping System
    MDPI AG ; 2016
    In:  Sensors Vol. 16, No. 2 ( 2016-01-28), p. 164-
    Details
    In: Sensors, MDPI AG, Vol. 16, No. 2 ( 2016-01-28), p. 164-
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s16020164
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2052857-7
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  • 7
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    Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning
    Copernicus GmbH ; 2018
    In:  Biogeosciences Vol. 15, No. 23 ( 2018-12-13), p. 7347-7377
    Details
    In: Biogeosciences, Copernicus GmbH, Vol. 15, No. 23 ( 2018-12-13), p. 7347-7377
    Abstract: Abstract. In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive random forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn nodules. Within the area considered in this study, Mn nodules show a heterogeneous and spatially clustered pattern, and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree), and the number of predictor variables to be randomly selected at each node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground-truth data acquired by box coring. Linking optical and hydroacoustic data revealed a nonlinear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modeling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modeling input for the RF.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    URL: Article
    DOI: 10.5194/bg-15-7347-2018
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2158181-2
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  • 8
    Online Resource
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    Compact-Morphology-based poly-metallic Nodule Delineation
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-10-17)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-10-17)
    Abstract: Poly-metallic nodules are a marine resource considered for deep sea mining. Assessing nodule abundance is of interest for mining companies and to monitor potential environmental impact. Optical seafloor imaging allows quantifying poly-metallic nodule abundance at spatial scales from centimetres to square kilometres. Towed cameras and diving robots acquire high-resolution imagery that allow detecting individual nodules and measure their sizes. Spatial abundance statistics can be computed from these size measurements, providing e.g. seafloor coverage in percent and the nodule size distribution. Detecting nodules requires segmentation of nodule pixels from pixels showing sediment background. Semi-supervised pattern recognition has been proposed to automate this task. Existing nodule segmentation algorithms employ machine learning that trains a classifier to segment the nodules in a high-dimensional feature space. Here, a rapid nodule segmentation algorithm is presented. It omits computation-intense feature-based classification and employs image processing only. It exploits a nodule compactness heuristic to delineate individual nodules. Complex machine learning methods are avoided to keep the algorithm simple and fast. The algorithm has successfully been applied to different image datasets. These data sets were acquired by different cameras, camera platforms and in varying illumination conditions. Their successful analysis shows the broad applicability of the proposed method.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-017-13335-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 9
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    Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-09-12)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-09-12)
    Abstract: Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible. Automated workflows have been proposed as a solution, in which algorithms assign pre-defined seafloor categories to each image. However, in order to provide consistent and repeatable analysis, these automated workflows need to address e.g., underwater illumination artefacts, variances in resolution and class-imbalances, which could bias the classification. Here, we present a generic implementation of an Automated and Integrated Seafloor Classification Workflow (AI-SCW). The workflow aims to classify the seafloor into habitat categories based on automated analysis of optical underwater images with only minimal amount of human annotations. AI-SCW incorporates laser point detection for scale determination and color normalization. It further includes semi-automatic generation of the training data set for fitting the seafloor classifier. As a case study, we applied the workflow to an example seafloor image dataset from the Belgian and German contract areas for Manganese-nodule exploration in the Pacific Ocean. Based on this, we provide seafloor classifications along the camera deployment tracks, and discuss results in the context of seafloor multibeam bathymetry. Our results show that the seafloor in the Belgian area predominantly comprises densely distributed nodules, which are intermingled with qualitatively larger-sized nodules at local elevations and within depressions. On the other hand, the German area primarily comprises nodules that only partly cover the seabed, and these occur alongside turned-over sediment (artificial seafloor) that were caused by the settling plume following a dredging experiment conducted in the area.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-022-19070-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 10
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    The 28‐year incidence of de novo malignancies after liver transplantation: A single‐center analysis of risk factors and mortality in 1616 patients
    Ovid Technologies (Wolters Kluwer Health) ; 2017
    In:  Liver Transplantation Vol. 23, No. 11 ( 2017-11), p. 1404-1414
    Details
    In: Liver Transplantation, Ovid Technologies (Wolters Kluwer Health), Vol. 23, No. 11 ( 2017-11), p. 1404-1414
    Abstract: De novo malignancies (DNMs) are one of the leading causes of late mortality after liver transplantation (LT). We analyzed 1616 consecutive patients who underwent LT between 1988 and 2006 at our institution. All patients were prospectively observed over a study period of 28 years by our own outpatient clinic. Complete follow‐up data were available for 96% of patients, 3% were incomplete, and only 1% were lost to follow‐up. The median follow‐up of the patients was 14.1 years. Variables with possible prognostic impact on the development of DNMs were analyzed, as was the incidence of malignancies compared with the nontransplant population by using standardized incidence ratios. In total, 266 (16.5%) patients developed 322 DNMs of the following subgroups: hematological malignancies (n = 49), skin cancer (n = 83), and nonskin solid organ tumors (SOT; n = 190). The probability of developing any DNM within 10 and 25 years was 12.9% and 23.0%, respectively. The respective probability of developing SOT was 7.8% and 16.2%. Mean age at time of diagnosis of SOT was 57.4 years (range, 18.3‐81.1 years). In the multivariate analysis, an increased recipient age (hazard ratio [HR], 1.03; P   〈  0.001) and a history of smoking (HR, 1.92; P   〈  0.001) were significantly associated with development of SOT. Moreover, the development of SOT was significantly increased in cyclosporine A–treated compared with tacrolimus‐treated patients (HR, 1.53; P  = 0.03). The present analysis shows a disproportionate increase of de novo SOT with an increasing follow‐up period. Increased age and a history of smoking are confirmed as major risk factors. Moreover, the importance of immunosuppression is highlighted. Liver Transplantation 23 1404–1414 2017 AASLD.
    Type of Medium: Online Resource
    ISSN: 1527-6465 , 1527-6473
    URL: Article
    DOI: 10.1002/lt.24795
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2017
    detail.hit.zdb_id: 2002186-0
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