Automated Image Analysis to Map the Extent of Deep-Sea Mining Plumes Reveals a Larger Impacted Area Compared to Manual Analysis

Abstract Deep sea mining for poly-metallic nodules impacts the environment in many ways. A key potential hazard is the creation of a sediment plume from resuspending sediment during seabed mining. The resuspended matter disperses with currents but eventually resettles on the seabed. Resettling causes a blanketing of the seafloor environment, potentially causing harm to in-, epi- and hyperbenthic communities with possible cascading effects into food webs of deep sea habitats. Mapping the extent of such blanketing is thus an important factor in quantifying potential impacts of deep-sea mining.One technology that can assess seabed blanketing is optical imaging with cameras at square-kilometre scale. To efficiently analyse the resulting Terabytes of image data with minimized bias, automated image analysis is required. Moreover, effective quantitative monitoring of the blanketing requires ground truthing of the image data. Here, we present results from a camera-based monitoring of a deep-sea mining simulation combined with automated image analysis using the CoMoNoD method and low-cost seabed sediment traps for quantification of the blanketing thickness. We found that the impacted area was about 50 percent larger than previously determined by manual image annotation.

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Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach

Remote Sensing ◽

10.3390/rs12030489 ◽

2020 ◽

Vol 12 (3) ◽

pp. 489 ◽

Cited By ~ 8

Author(s):

Manuel González-Rivero ◽

Oscar Beijbom ◽

Alberto Rodriguez-Ramirez ◽

Dominic E. P. Bryant ◽

Anjani Ganase ◽

...

Keyword(s):

Artificial Intelligence ◽

Image Analysis ◽

Coral Reef ◽

Coral Reefs ◽

Image Annotation ◽

Rapid Evolution ◽

Modern Society ◽

Automated Image Analysis ◽

Ecosystem Monitoring ◽

Reef Monitoring

Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas.

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Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

PLoS ONE ◽

10.1371/journal.pone.0038179 ◽

2012 ◽

Vol 7 (6) ◽

pp. e38179 ◽

Cited By ~ 58

Author(s):

Timm Schoening ◽

Melanie Bergmann ◽

Jörg Ontrup ◽

James Taylor ◽

Jennifer Dannheim ◽

...

Keyword(s):

Image Analysis ◽

Deep Sea ◽

Automated Image Analysis ◽

The Arctic

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A workflow for low-cost automated image analysis of myxomycete spore numbers, size and shape

PeerJ ◽

10.7717/peerj.12471 ◽

2021 ◽

Vol 9 ◽

pp. e12471

Author(s):

Jan Woyzichovski ◽

Oleg Shchepin ◽

Nikki Heherson Dagamac ◽

Martin Schnittler

Keyword(s):

Image Analysis ◽

Size Distribution ◽

Low Cost ◽

Single Layer ◽

Automated Image Analysis ◽

Successful Implementation ◽

Spore Size ◽

Size And Shape ◽

Exact Size ◽

Learning Software

Measuring spore size is a standard method for the description of fungal taxa, but in manual microscopic analyses the number of spores that can be measured and information on their morphological traits are typically limited. To overcome this weakness we present a method to analyze the size and shape of large numbers of spherical bodies, such as spores or pollen, by using inexpensive equipment. A spore suspension mounted on a slide is treated with a low-cost, high-vibration device to distribute spores uniformly in a single layer without overlap. Subsequently, 10,000 to 50,000 objects per slide are measured by automated image analysis. The workflow involves (1) slide preparation, (2) automated image acquisition by light microscopy, (3) filtering to separate high-density clusters, (4) image segmentation by applying a machine learning software, Waikato Environment for Knowledge Analysis (WEKA), and (5) statistical evaluation of the results. The technique produced consistent results and compared favorably with manual measurements in terms of precision. Moreover, measuring spore size distribution yields information not obtained by manual microscopic analyses, as shown for the myxomycete Physarum albescens. The exact size distribution of spores revealed irregularities in spore formation resulting from the influence of environmental conditions on spore maturation. A comparison of the spore size distribution within and between sporocarp colonies showed large environmental and likely genetic variation. In addition, the comparison identified specimens with spores roughly twice the normal size. The successful implementation of the presented method for analyzing myxomycete spores also suggests potential for other applications.

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Automation strategies for large-scale 3D image analysis

at - Automatisierungstechnik ◽

10.1515/auto-2016-0019 ◽

2016 ◽

Vol 64 (7) ◽

Cited By ~ 1

Author(s):

Johannes Stegmaier ◽

Benjamin Schott ◽

Eduard Hübner ◽

Manuel Traub ◽

Maryam Shahid ◽

...

Keyword(s):

Image Analysis ◽

Large Scale ◽

Imaging Techniques ◽

Image Data ◽

Automated Image Analysis ◽

Future Research ◽

Time Resolved ◽

Processing Strategies ◽

Spatio Temporal ◽

Automation Strategies

AbstractNew imaging techniques enable visualizing and analyzing a multitude of unknown phenomena in many areas of science at high spatio-temporal resolution. The rapidly growing amount of image data, however, can hardly be analyzed manually and, thus, future research has to focus on automated image analysis methods that allow one to reliably extract the desired information from large-scale multidimensional image data. Starting with infrastructural challenges, we present new software tools, validation benchmarks and processing strategies that help coping with large-scale image data. The presented methods are illustrated on typical problems observed in developmental biology that can be answered, e.g., by using time-resolved 3D microscopy images.

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Automated Image Analysis for High-Content Screening and Analysis

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057110370894 ◽

2010 ◽

Vol 15 (7) ◽

pp. 726-734 ◽

Cited By ~ 81

Author(s):

Aabid Shariff ◽

Joshua Kangas ◽

Luis Pedro Coelho ◽

Shannon Quinn ◽

Robert F. Murphy

Keyword(s):

Machine Learning ◽

Image Processing ◽

Image Analysis ◽

Cell Biology ◽

Image Data ◽

Automated Analysis ◽

Image Features ◽

Automated Image Analysis ◽

High Content Screening ◽

Spatial Transformation

The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.

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Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078638 ◽

1977 ◽

Vol 35 ◽

pp. 220-221

Author(s):

S.F. Stinson ◽

J.C. Lilga ◽

M.B. Sporn

Keyword(s):

Image Analysis ◽

Pattern Analysis ◽

Relative Proportion ◽

Automated Image Analysis ◽

Image Analyzer ◽

Cross Sectional ◽

Exfoliated Cells ◽

Neoplastic Lesions ◽

Analysis System ◽

Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

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Progress In The Practical Application Of Automated Image Analysis To Tem Negatives

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078614 ◽

1977 ◽

Vol 35 ◽

pp. 214-217

Author(s):

F. A. Heckman ◽

E. Redman ◽

J.E. Connolly

Keyword(s):

Image Analysis ◽

Image Quality ◽

Exposure Time ◽

Image Contrast ◽

Automated Image Analysis ◽

Practical Application ◽

Factors Affecting ◽

High Image Quality ◽

Qualitative Evidence ◽

Comprehensive Study

In our initial publication on this subject1) we reported results demonstrating that contrast is the most important factor in producing the high image quality required for reliable image analysis. We also listed the factors which enhance contrast in order of the experimentally determined magnitude of their effect. The two most powerful factors affecting image contrast attainable with sheet film are beam intensity and KV. At that time we had only qualitative evidence for the ranking of enhancing factors. Later we carried out the densitometric measurements which led to the results outlined below.Meaningful evaluations of the cause-effect relationships among the considerable number of variables in preparing EM negatives depend on doing things in a systematic way, varying only one parameter at a time. Unless otherwise noted, we adhered to the following procedure evolved during our comprehensive study:Philips EM-300; 30μ objective aperature; magnification 7000- 12000X, exposure time 1 second, anti-contamination device operating.

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Interfacing of a TEM/STEM System to a Computer

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097818 ◽

1981 ◽

Vol 39 ◽

pp. 250-251

Author(s):

P. Hagemann

Keyword(s):

Image Analysis ◽

Transmission Rate ◽

Analytical Electron Microscopy ◽

Signal Beam ◽

Automated Image Analysis ◽

Beam Deflection ◽

External Control ◽

Computer Input ◽

Instrument Control ◽

Data Acquisition And Processing

The use of computers in the analytical electron microscopy today shows three different trends (1) automated image analysis with dedicated computer systems, (2) instrument control by microprocessors and (3) data acquisition and processing e.g. X-ray or EEL Spectroscopy.While image analysis in the T.E.M. usually needs a television chain to get a sequential transmission suitable as computer input, the STEM system already has this necessary facility. For the EM400T-STEM system therefore an interface was developed, that allows external control of the beam deflection in TEM as well as the control of the STEM probe and video signal/beam brightness on the STEM screen.The interface sends and receives analogue signals so that the transmission rate is determined by the convertors in the actual computer periphery.

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High-speed brightfield 3-D imaging and 3-D image analysis of thick and overlapped cell clusters in cytological preparations

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100168827 ◽

1994 ◽

Vol 52 ◽

pp. 218-219

Author(s):

Robert W. Mackin

Keyword(s):

Image Analysis ◽

High Speed ◽

Three Dimensional ◽

Image Data ◽

Ccd Camera ◽

Objective Lens ◽

Array Processor ◽

Vaginal Smears ◽

Low Contrast ◽

Computer Controlled

This paper presents two advances towards the automated three-dimensional (3-D) analysis of thick and heavily-overlapped regions in cytological preparations such as cervical/vaginal smears. First, a high speed 3-D brightfield microscope has been developed, allowing the acquisition of image data at speeds approaching 30 optical slices per second. Second, algorithms have been developed to detect and segment nuclei in spite of the extremely high image variability and low contrast typical of such regions. The analysis of such regions is inherently a 3-D problem that cannot be solved reliably with conventional 2-D imaging and image analysis methods.High-Speed 3-D imaging of the specimen is accomplished by moving the specimen axially relative to the objective lens of a standard microscope (Zeiss) at a speed of 30 steps per second, where the stepsize is adjustable from 0.2 - 5μm. The specimen is mounted on a computer-controlled, piezoelectric microstage (Burleigh PZS-100, 68/μm displacement). At each step, an optical slice is acquired using a CCD camera (SONY XC-11/71 IP, Dalsa CA-D1-0256, and CA-D2-0512 have been used) connected to a 4-node array processor system based on the Intel i860 chip.

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