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  • Springer Science and Business Media LLC  (15)
  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2018
    In:  Marine Geophysical Research Vol. 39, No. 1-2 ( 2018-6), p. 289-306
    In: Marine Geophysical Research, Springer Science and Business Media LLC, Vol. 39, No. 1-2 ( 2018-6), p. 289-306
    Type of Medium: Online Resource
    ISSN: 0025-3235 , 1573-0581
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 414196-9
    detail.hit.zdb_id: 1478200-5
    SSG: 16,13
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-10-17)
    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
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 3
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2018
    In:  Journal of Ocean University of China Vol. 17, No. 3 ( 2018-6), p. 555-562
    In: Journal of Ocean University of China, Springer Science and Business Media LLC, Vol. 17, No. 3 ( 2018-6), p. 555-562
    Type of Medium: Online Resource
    ISSN: 1672-5182 , 1993-5021
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2274194-X
    SSG: 14
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Reports Vol. 13, No. 1 ( 2023-05-23)
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-05-23)
    Abstract: Recent advances in optical underwater imaging technologies enable the acquisition of huge numbers of high-resolution seafloor images during scientific expeditions. While these images contain valuable information for non-invasive monitoring of megabenthic fauna, flora and the marine ecosystem, traditional labor-intensive manual approaches for analyzing them are neither feasible nor scalable. Therefore, machine learning has been proposed as a solution, but training the respective models still requires substantial manual annotation. Here, we present an automated image-based workflow for Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast). The workflow significantly reduces the required annotation effort by automating the detection of anomalous superpixels, which are regions in underwater images that have unusual properties relative to the background seafloor. The bounding box coordinates of the detected anomalous superpixels are proposed as a set of weak annotations, which are then assigned semantic morphotype labels and used to train a Faster R-CNN object detection model. We applied this workflow to example underwater images recorded during cruise SO268 to the German and Belgian contract areas for Manganese-nodule exploration, within the Clarion–Clipperton Zone (CCZ). A performance assessment of our FaunD-Fast model showed a mean average precision of 78.1% at an intersection-over-union threshold of 0.5, which is on a par with competing models that use costly-to-acquire annotations. In more detail, the analysis of the megafauna detection results revealed that ophiuroids and xenophyophores were among the most abundant morphotypes, accounting for 62% of all the detections within the surveyed area. Investigating the regional differences between the two contract areas further revealed that both megafaunal abundance and diversity was higher in the shallower German area, which might be explainable by the higher food availability in form of sinking organic material that decreases from east-to-west across the CCZ. Since these findings are consistent with studies based on conventional image-based methods, we conclude that our automated workflow significantly reduces the required human effort, while still providing accurate estimates of megafaunal abundance and their spatial distribution. The workflow is thus useful for a quick but objective generation of baseline information to enable monitoring of remote benthic ecosystems.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2615211-3
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  • 5
    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
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 6
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2018
    In:  Scientific Data Vol. 5, No. 1 ( 2018-08-28)
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 5, No. 1 ( 2018-08-28)
    Abstract: Optical imaging is a common technique in ocean research. Diving robots, towed cameras, drop-cameras and TV-guided sampling gear: all produce image data of the underwater environment. Technological advances like 4K cameras, autonomous robots, high-capacity batteries and LED lighting now allow systematic optical monitoring at large spatial scale and shorter time but with increased data volume and velocity. Volume and velocity are further increased by growing fleets and emerging swarms of autonomous vehicles creating big data sets in parallel. This generates a need for automated data processing to harvest maximum information. Systematic data analysis benefits from calibrated, geo-referenced data with clear metadata description, particularly for machine vision and machine learning. Hence, the expensive data acquisition must be documented, data should be curated as soon as possible, backed up and made publicly available. Here, we present a workflow towards sustainable marine image analysis. We describe guidelines for data acquisition, curation and management and apply it to the use case of a multi-terabyte deep-sea data set acquired by an autonomous underwater vehicle.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2775191-0
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  • 7
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Data Vol. 10, No. 1 ( 2023-05-25)
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2023-05-25)
    Abstract: We provide a sequence of analysis-ready optical underwater images from the Clarion-Clipperton Zone (CCZ) of the Pacific Ocean. The images were originally recorded using a towed camera sledge that photographed a seabed covered with polymetallic manganese-nodules, at an average water depth of 4,250 meters. The original degradation in visual quality and inconsistent scale among individual raw images due to different altitude implies that they are not scientifically comparable in their original form. Here, we present analysis-ready images that have already been pre-processed to account for this degradation. We also provide accompanying metadata for each image, which includes their geographic coordinates, depth of the seafloor, absolute scale (cm/pixel), and seafloor habitat class obtained from a previous study. The provided images are thus directly usable by the marine scientific community e.g., to train machine learning models for seafloor substrate classification and megafauna detection.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2775191-0
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  • 8
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2013
    In:  The ISME Journal Vol. 7, No. 4 ( 2013-4), p. 685-696
    In: The ISME Journal, Springer Science and Business Media LLC, Vol. 7, No. 4 ( 2013-4), p. 685-696
    Type of Medium: Online Resource
    ISSN: 1751-7362 , 1751-7370
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
    detail.hit.zdb_id: 2299378-2
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  • 9
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2010
    In:  Swiss Journal of Geosciences Vol. 103, No. 1 ( 2010-07), p. 33-42
    In: Swiss Journal of Geosciences, Springer Science and Business Media LLC, Vol. 103, No. 1 ( 2010-07), p. 33-42
    Type of Medium: Online Resource
    ISSN: 1661-8726 , 1661-8734
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2010
    detail.hit.zdb_id: 2379946-8
    SSG: 13
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  • 10
    In: Trials, Springer Science and Business Media LLC, Vol. 24, No. 1 ( 2023-04-05)
    Abstract: Patients with cirrhosis and ascites (and portal hypertension) are at risk of developing acute kidney injury (AKI). Although many etiologies exist, hepatorenal AKI (HRS-AKI) remains a frequent and difficult-to-treat cause, with a very high mortality when left untreated. The standard of care is the use of terlipressin and albumin. This can lead to reversal of AKI, which is associated to survival. Nevertheless, only approximately half of the patients achieve this reversal and even after reversal patients remains at risk for new episodes of HRS-AKI. TIPS is accepted for use in patients with variceal bleeding and refractory ascites, which leads to a reduction in portal pressure. Although preliminary data suggest it may be useful in HRS-AKI, its use in this setting is controversial and caution is recommended given the fact that HRS-AKI is associated to cardiac alterations and acute-on-chronic liver failure (ACLF) which represent relative contraindications for transjugular intrahepatic portosystemic shunt (TIPS). In the last decades, with the new definition of renal failure in patients with cirrhosis, patients are identified at an earlier stage. These patients are less sick and therefore more likely to not have contraindications for TIPS. We hypothesize that TIPS could be superior to the standard of care in patients with HRS-AKI. Methods This study is a prospective, multicenter, open, 1:1-randomized, controlled parallel-group trial. The main end-point is to compare the 12-month liver transplant-free survival in patients assigned to TIPS compared to the standard of care (terlipressin and albumin). Secondary end-point include reversal of HRS-AKI, health-related Quality of Life (HrQoL), and incidence of further decompensation among others. Once patients are diagnosed with HRS-AKI, they will be randomized to TIPS or Standard of Care (SOC). TIPS should be placed within 72 h. Until TIPS placement, TIPS patients will be treated with terlipressin and albumin. Once TIPS is placed, terlipressin and albumin should be weaned off according to the attending physician. Discussion If the trial were to show a survival advantage for patients who undergo TIPS placement, this could be incorporated in routine clinical practice in the management of patients with HRS-AKI. Trial registration Clinicaltrials.gov NCT05346393 . Released to the public on 01 April 2022.
    Type of Medium: Online Resource
    ISSN: 1745-6215
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2040523-6
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