Fully automatic detection and classification of phytoplankton specimens in digital microscopy images

©2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article Rivas-Villar, D., Rouco, J., Carballeira, R., Penedo, M. G., & Novo, J. (2021). “Fully automatic detection and classification of phytoplankton specimens in digital microscopy images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 200(105923), 105923. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2020.105923.[Abstract]: Background and objective: The proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purification techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents a major limitation. The adequate identification and classification of phytoplankton specimens requires intensive training and expertise. Additionally, the performed analysis involves a lengthy process that exhibits serious problems of reliability and repeatability as inter-expert agreement is not always reached. Considering all those factors, the automatization of these analyses is, therefore, highly desirable to reduce the workload of the specialists and facilitate the process. Methods: This manuscript proposes a novel fully automatic methodology to perform phytoplankton analyses in digital microscopy images of water samples taken with a regular light microscope. In particular, we propose a method capable of analysing multi-specimen images acquired using a simplified systematic protocol. In contrast with prior approaches, this enables its use without the necessity of an expert taxonomist operating the microscope. The system is able to detect and segment the different existing phytoplankton specimens, with high variability in terms of visual appearances, and to merge them into colonies and sparse specimens when necessary. Moreover, the system is capable of differentiating them from other similar objects like zooplankton, detritus or mineral particles, among others, and then classify the specimens into defined target species of interest using a machine learning-based approach. Results: The proposed system provided satisfactory and accurate results in every step. The detection step provided a FNR of 0.4%. Phytoplankton detection, that is, differentiating true phytoplankton from similar objects (zooplankton, minerals, etc.), provided a result of 84.07% of precision at 90% of recall. The target species classification, reported an overall accuracy of 87.50%. The recall levels for each species are, 81.82% for W. naegeliana, 57.15% for A. spiroides, 85.71% for D. sociale and 95% for the ”Other” group, a set of relevant toxic and interesting species widely spread over the samples. Conclusions: The proposed methodology provided accurate results in all the designed steps given the complexity of the problem, particularly in terms of specimen identification, phytoplankton differentiation as well as the classification of the defined target species. Therefore, this fully automatic system represents a robust and consistent tool to aid the specialists in the analysis of the quality of the water sources and potability.This work is supported by the European Regional Development Fund (ERDF) of the European Union and Xunta de Galicia through Centro de Investigación del Sistema Universitario de Galicia, ref. ED431G 2019/01.Xunta de Galicia; ED431G 2019/0

THE DEVELOPMENT OF NOVEL TECHNIQUES FOR CHARACTERISATION OF MARINE ZOOPLANKTON OVER VERY LARGE SPATIAL SCALES

Marine zooplankton play an important role in the transfer of CO2 from the atmosphere/ocean system to deeper waters and the sediments. They also provide food for much of the world's fish stocks and in some areas of the ocean depleted of nutrients they sustain phytoplankton growth by recycling nutrients. They therefore have a profound effect on the carbon cycle and upon life in the oceans. There is a perceived lack of information about global distributions of zooplankton needed to validate ecosystems dynamics models, and the traditional methods of survey are inadequate to provide this information. There is a need to develop new technologies for the large scale survey of zooplankton, which should provide data either suitable for quick and easy subsequent processing, or better still, processed in real time. New technologies for large scale zooplankton survey fall into three main categories: acoustic, optical and video. No single method is capable of providing continuous real time data at the level of detail required. A combination of two of the new technologies (optical and video) has the potential to provide broad scale data on abundance, size and species distributions of zooplankton routinely, reliably, rapidly and economically. Such a combined method has been developed in this study. The optical plankton counter (OPC) is a fairly well established instrument in marine and freshwater zooplankton survey. A novel application of the benchtop version of this instrument (OPC-IL) for real time data gathering at sea over ocean basin scales has been developed in this study. A new automated video zooplankton analyser (ViZA) has been designed and developed to operate together with the OPC-IL. The two devices are eventually to be deployed in tandem on the Undulating Oceanographic Recorder (UOR) for large scale ocean survey of zooplankton. During the initial development of the system, the two devices are used in benchtop flow through mode using the ship's uncontaminated sea water supply. The devices have been deployed on four major oceanographic cruises in the North and South Atlantic, covering almost 40,000 km. of transect. Used in benchtop mode, it has been shown that the OPC can simply and reliably survey thousands of kilometres of ocean surface waters for zooplankton abundance and size distribution in the size range 250|im. to 11.314 mm. in real time. The ViZA system can add the dimension of shape to the OPC size data, and provide supporting data on size distributions and abundance. Sampling rate in oligotrophic waters, and image quality problems are two main limitations to current ViZA performance which must be addressed, but where sufficient abundance exists and good quality images are obtained, the initial version of the ViZA system is shown to be able reliably to classify zooplankton to six major groups. The four deployments have shown that data on zooplankton distributions on oceanic scales can be obtained without the delays and prohibitive costs associated with sample analysis for traditional sampling methods. The results of these deployments are presented, together with an assessment of the performance of the system and proposals for improvements to meet the requirements specified before a fiill in-situ system is deployed.Plymouth Marine Laborator

Automatische bildbasierte Segmentierung organischer Objekte einer gleichartigen Gruppe: Abgeleitet vom Problem der Stammschnittflächensegmentierung

Diese Arbeit adressiert die automatische bildbasierte Segmentierung von organo-Gruppen, Gruppen gleichartiger organischer Objekte. Die Segmentierung einer organo-Gruppe ermöglicht Anwendungen zur automatischen Vermessung, Inspektion oder Sortierung. In dieser Arbeit werden, ausgehend vom Problem der Stammschnittflächen, drei Segmentierungskonzepte entwickelt und quantitativ evaluiert. Ausgehend von den Konzepten wird eine allgemeinere Lösung für organo-Gruppen entwickelt und am Beispiel von Plattfischen, Kartoffeln und Äpfeln evaluiert, wobei gute bis sehr gute Ergebnisse erzielt werden

Image segmentation using graph neural networks

Táto diplomová práca opisuje a implementuje návrh grafovej neurónovej siete použitej na 2D segmentáciu nervovej štruktúry. Prvá kapitola práce v krátkosti uvádza problém segmentácie. Nachádza sa v nej rozdelenie segmentačných techník podľa princípov metód, ktoré používajú. Každý typ techniky obsahuje podstatu tejto kategórie a taktiež popis jedného predstaviteľa. Druhá kapitola diplomovej práce sa zaoberá grafovými neurónovými sieťami (v skratke GNN). Tu práca rozdeľuje grafové neurónové siete celkovova bližšie popisuje rekurentné grafové neurónové siete (v skratke RGNN) a grafové autoenkodéry, ktoré je možné použiť na segmentáciu obrazu. Konkrétne riešenie segmentácie obrazu je založené na metóde prenášania správ v RGNN, ktorou je možné nahradiť konvolučné masky v konvolučných neurónových sieťach. RGNN taktiež používajú jednoduchšiu topológiu viacvrstvového perceptronu. Druhým typom opísaných grafových neurónových sietí v práci sú grafové autoenkodéry, ktoré použivajú rôzne metódy pre lepšie zakódovanie vrcholov grafu do euklidovského priestoru. Posledná časť diplomovej prace sa venuje rozboru problému, návrhu jeho konkrétneho riešenia a vyhodnotenie výsledkov. Úlohou praktickej časti práce je implementácia GNN na segmentáciu obrazových dát. Výhodou použitia neurónových sietí je možnosť riešiť rozličné typy segmentácie zmenou trénovacích dát. Na implementáciu GNN bola použitá RGNN s prenášaním správ a autoenkodér node2vec. Trénovanie RGNN bolo uskutočnené na grafických kartách poskytnutých školou a službou Google Colaboratory. Učenie RGNN s použitím node2vec bolo pamäťovo veľmi náročné, a preto bolo nutné trénovanie na procesore s operačnou pamäťou vačšou ako 12GB. V rámci optimalizácie RGNN bolo otestované učenie použitím rôznych stratových funkcií, zmenou topológie a učiacich parametrov. Pre použitie node2vec bola vytvorená metóda stromovej štruktúry na zlepšenie segmentácie, avšak výsledky nepotvrdili zlepšenie pre malý počet iterácií. Najlepšie výsledky praktickej implementácie boli vyhodnotené pomocou porovnania otestovaných dát s konvolučnou neurónovu sieťou U-Net. Je možné skonštatovať porovnaťeľné výsledky oproti sieti U-Net, avšak pre porovnanie týchto sietí je potrebné daľšie testovanie. Výsledkom práce je použitie RGNNako moderné riešenie problému segmentácie obrazu a poskytnutie základu pre daľší výskum.This diploma thesis describes and implements the design of a graph neural network usedfor 2D segmentation of neural structure. The first chapter of the thesis briefly introduces the problem of segmentation. In this chapter, segmentation techniques are divided according to the principles of the methods they use. Each type of technique contains the essence of this category as well as a description of one representative. The second chapter of the diploma thesis explains graph neural networks (GNN for short). Here, the thesis divides graph neural networks in general and describes recurrent graph neural networks(RGNN for short) and graph autoencoders, that can be used for image segmentation, in more detail. The specific image segmentation solution is based on the message passing method in RGNN, which can replace convolution masks in convolutional neural networks.RGNN also provides a simpler multilayer perceptron topology. The second type of graph neural networks characterised in the thesis are graph autoencoders, which use various methods for better encoding of graph vertices into Euclidean space. The last part ofthe diploma thesis deals with the analysis of the problem, the proposal of its specific solution and the evaluation of results. The purpose of the practical part of the work was the implementation of GNN for image data segmentation. The advantage of using neural networks is the ability to solve different types of segmentation by changing training data. RGNN with messaging passing and node2vec were used as implementation GNNf or segmentation problem. RGNN training was performed on graphics cards provided bythe school and Google Colaboratory. Learning RGNN using node2vec was very memory intensive and therefore it was necessary to train on a processor with an operating memory larger than 12GB. As part of the RGNN optimization, learning was tested using various loss functions, changing topology and learning parameters. A tree structure method was developed to use node2vec to improve segmentation, but the results did not confirman improvement for a small number of iterations. The best outcomes of the practical implementation were evaluated by comparing the tested data with the convolutional neural network U-Net. It is possible to state comparable results to the U-Net network, but further testing is needed to compare these neural networks. The result of the thesisis the use of RGNN as a modern solution to the problem of image segmentation and providing a foundation for further research.

Automatische bildbasierte Segmentierung organischer Objekte einer gleichartigen Gruppe: Abgeleitet vom Problem der Stammschnittflächensegmentierung

Diese Arbeit adressiert die automatische bildbasierte Segmentierung von organo-Gruppen, Gruppen gleichartiger organischer Objekte. Die Segmentierung einer organo-Gruppe ermöglicht Anwendungen zur automatischen Vermessung, Inspektion oder Sortierung. In dieser Arbeit werden, ausgehend vom Problem der Stammschnittflächen, drei Segmentierungskonzepte entwickelt und quantitativ evaluiert. Ausgehend von den Konzepten wird eine allgemeinere Lösung für organo-Gruppen entwickelt und am Beispiel von Plattfischen, Kartoffeln und Äpfeln evaluiert, wobei gute bis sehr gute Ergebnisse erzielt werden

Investigations into zooplankton assemblages off the west coast of Scotland

Zooplankton assemblages were examined from waters off the west coast of Scotland encompassing the Firths of Lorn and Clyde, the North Channel, and the Malin Shelf. Size fractionated samples (coarse, >1000μm; medium, 1000μm-330μm; fine, 330μm-180μm) were collected with a submersible pump from 10m and 30m depth in March (1987) and May (1986) providing a composite picture of the fauna in early and late spring conditions, respectively. The feasibility of using image analysis as a method for processing zooplankton samples was examined. Although a programme was successfully operated to obtain individual measurement data, much work is still required before a fully automated programme for routine use by planktologists is available. Total zooplankton numbers and biomass, and species distributions and relative abundances were examined. Species assemblages were identified using multivariate analyses. Biomass and abundance spectra by size were examined for the major station groupings. In general, meroplankton dominated the fauna in the Firth of Lorn while large numbers of Calanus spp. occurred in the Firth of Clyde. Small copepods such as Oithona spp. were characteristic of the assemblage on the Malin Shelf. Salinity, followed by temperature, showed the strongest association with the observed station clusters. Chlorophyll a and depth did not generally appear to influence station groupings. The potential for the mixing and exchange of zooplankton between the regions of the study area was evaluated. The results suggest that zooplankton may be entrained from the Firth of Clyde by the Scottish Coastal Current during the spring period. The Malin Shelf may also be an important source of zooplankton for the Firth of Lorn during winter months when an onshore flow of Atlantic water occurs

Water quality modeling of Lake Diefenbaker

Lake Diefenbaker is one of the most important sources of water in the prairie province of Saskatchewan, Canada. It is a long (181.6 km) and narrow (maximum width 6 km) reservoir formed along the South Saskatchewan River by the construction of the Gardiner and Qu'Appelle River dams in the 1960s. The reservoir has a surface elevation of 556.87 meters above sea level (full supply level) with a maximum depth of 60 m, a surface area of approximately 393 km2 and a volume of 9.03 km3. The reservoir and dams are part of a multipurpose hydraulic project, which provides water for irrigation, drinking water, eco-services, hydropower generation, aquaculture and recreation as well as for flood mitigation. Surface water quality modeling is a useful tool to simulate and predict nutrient dynamics in lakes, reservoirs, and rivers, as well as the fate and transport of sediment and toxic contaminants in freshwater environments. In this study, water quality modeling of Lake Diefenbaker was carried out in order to help understand the mixing regimes and biological processes in the aquatic environment of this strategic reservoir. Based on the study's objectives, the physical and chemical characteristics of the lake and available data, the laterally-averaged two-dimensional model CE-QUAL-W2 hydrodynamic and water quality model was deemed the best model for Lake Diefenbaker. CE-QUAL-W2 was developed by the US Army Corp of Engineers to simulate the hydrodynamics, water quality, aquatic biology and aquatic chemistry in surface waters. On the one hand, this study provided information on temperature and hydrodynamic behaviors of Lake Diefenbaker as well as sediment and nutrient transport, nutrient uptake and algal activities. On the other hand, it addressed some key and limitations in the application of water quality models. Limitations addressed include studying snow cover effects on the ice surface in winter, applying variable algal stoichiometry, using combined local/global optimization for model calibration, and running the model on High-Performance Cluster (HPC) systems

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels