Automated Diatom Classification (Part B): A Deep Learning Approach

Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).The authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO) http://aqualitas-retos.es/en/

Automated Diatom Classification (Part B): A Deep Learning Approach

This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images[EN] Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classificationSIThe authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO) http://aqualitas-retos.es/en

Lights and pitfalls of convolutional neural networks for diatom identification

P. 1-10Diatom detection has been a challenging task for computer scientist and biologist during past years. In this work, the new state of art techniques based on the deep learning framework have been tested, in order to check whether they are suitable for this purpose. On the one hand, RCNNs (Region based Convolutional Neural Networks), which select candidate regions and applies a convolutional neural network and, on the other hand, YOLO (You Only Look Once), which applies a single neural network over the whole image, have been tested. The first one is able to reach poor results in out experimentation, with an average of 0.68 recall and some tricky aspects, as for example it is needed to apply a bounding box merging algorithm to get stable detections; but the second one gets remarkable results, with an average of 0.84 recall in the evaluation that have been carried out, and less aspects to take into account after the detection has been performed. Future work related to parameter tuning and processing are needed to increase the performance of deep learning in the detection task. However, as for classification it has been probed to provide succesfully performance.S

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images[EN] This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future workSIThe authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO), http://aqualitas-retos.es/en

Deletion of lysophosphatidic acid receptor LPA1 reduces neurogenesis in the mouse dentate gyrus

Neurogenesis persists in certain regions of the adult brain including the subgranular zone of the hippocampal dentate gyrus wherein its regulation is essential, particularly in relation to learning, stress and modulation of mood. Lysophosphatidic acid (LPA) is an extracellular signaling phospholipid with important neural regulatory properties mediated by specific G protein-coupled receptors, LPA1–5. LPA1 is highly expressed in the developing neurogenic ventricular zone wherein it is required for normal embryonic neurogenesis, and, by extension may play a role in adult neurogenesis as well. By means of the analyses of a variant of the original LPA1-null mutant mouse, termed the Malaga variant or “maLPA1-null,” which has recently been reported to have defective neurogenesis within the embryonic cerebral cortex, we report here a role for LPA1 in adult hippocampal neurogenesis. Proliferation, differentiation and survival of newly formed neurons are defective in the absence of LPA1 under normal conditions and following exposure to enriched environment and voluntary exercise. Furthermore, analysis of trophic factors in maLPA1-null mice demonstrated alterations in brain-derived neurotrophic factor and insulin growth factor 1 levels after enrichment and exercise. Morphological analyses of doublecortin positive cells revealed the anomalous prevalence of bipolar cells in the subgranular zone, supporting the operation of LPA1 signaling pathways in normal proliferation, maturation and differentiation of neuronal precursors

Her2 challenge contest: a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues

Aims Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. Methods and Results The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the “ground truth” (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. Conclusions This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring

Automatic recognition of diatoms and its application to the study of water quality

Computer vision and artificial intelligence are some of the most powerful fields in computer science nowadays. They have the ability to change the world and therefore to develop solutions to some of the greatest challenges in our society. This project is focused in the study and application of image processing, detection and classification techniques, both using traditional and novel methods. Attention has been payed to the last ones. Comprehensive workflows have been developed to apply these techniques to a specific problem, that is, automatic diatom recognition. These algae are useful in monitoring the quality of water, hence the importance of automating the analysis process. The conventional approach has usually consisted of manual identification and quantification by optical microscopy; however there is a need for automated recognition techniques and diagnostic tools to facilitate proper management of water resources and decision-making processes. Moreover, it has been explored the applicability of the proposed approach in Anatomical Pathology for medical diagnosis using microscopic images. As a result, an international contest has been joined and research publications have started to be written. La visión por computador y la inteligencia artificial son algunos de los campos más potentes de la informática en la actualidad. Tienen la capacidad de cambiar el mundo y hacen posible el desarrollo de soluciones para los grandes retos de nuestra sociedad. Este proyecto está centrado en el estudio y aplicación de técnicas de procesado de imágenes, detección y clasificación, por medio de métodos tradicionales y novedosos, prestando especial atención a estos últimos. Se han desarrollado exhaustivos flujos de trabajo para la aplicación de estas técnicas a un problema concreto, el reconocimiento automático de diatomeas. Estas algas son útiles para monitorizar la calidad del agua, de ahí la importancia de automatizar el proceso de análisis. El método tradicional ha consistido habitualmente en la identificación y cuantificación manual por medio de microscopía óptica; sin embargo, hay una necesidad de técnicas de reconocimiento automatizado y herramientas de diagnóstico para facilitar una gestión adecuada de los recursos acuáticos y para apoyar los procesos de toma de decisiones. Además, se ha explorado la aplicación del enfoque propuesto en Anatomía Patológica para el diagnóstico médico por medio de imágenes microscópicas. Como resultado, se ha participado en un concurso internacional y ha comenzado la escritura de publicaciones en el ámbito científico

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.The authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO)We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

Poblaciones de fitonematodos asociados al vigor de plantas de plátano

Introduction. The biotic fraction of the soil is the essential component in ecological processes and can influence agricultural productivity of crops. Objective. To Identify the population of phytonematodes associated with plantain plants vigor in the southern zone of lake Maracaibo, Venezuela. Materials and methods. Eight 2-hectare batches of plantain plants (Musa AAB cv. Hartón), located in the south of lake Maracaibo, were selected and divided into two 1-hectare areas or lots, during October and December of the year 2018. The designation of the lots by vigor (HV= high vigor and BV= low vigor) was made based on the parameters of the number of hands per cluster, circumference of the mother plant, and the height of the son of succession, in twenty plants close to harvest, for each one. The soil sample was collected at a depth of 15 cm on three newly flowered plants per lot and for the roots a microcalicata was made per plant up to 30 cm deep. For the nematological extraction in the soil, the oostenbrink levigation method was used with a Baermann funnel and the blending method was used as a root. Subsequently, the specimens found were quantified by gender. Results. A population more significant (p<0,05) than in the root (2670 individuals) was found in the soil (14 140 individuals). No statistical difference between AV (395) and BV (305) was observed when comparing the total population by vigor. Twelve taxonomic genera were determined in the root and soil samples. The most prevalent specimens in AV were Helicotylenchus multicinctus and Radopholus similis and in the BV lots it was Pratylenchus sp. Conclusion. A high diversity of nematofauna present in both soils and roots of the crop was observed.Introducción. La fracción biótica del suelo es el componente esencial en los procesos ecológicos y pueden ejercer influencia en la productividad agrícola de los cultivos. Objetivo. Identificar la población de fitonematodos asociados al vigor de las plantas de plátano en la zona de sur del lago de Maracaibo, Venezuela. Materiales y métodos. Se seleccionaron ocho lotes de plantas de plátano (Musa AAB cv. Hartón) de 2 ha, ubicados en el sur del lago de Maracaibo, los cuales se dividieron en dos áreas o lotes de 1 ha, durante octubre y diciembre del año 2018. La designación de los lotes por vigor (AV= alto vigor y BV= bajo vigor) se realizó con base en los parámetros del número de manos por racimo, circunferencia de la planta madre y altura del hijo de sucesión, en veinte plantas próximas a la cosecha, por cada uno. La muestra de suelo se recolectó a 15 cm de profundidad en tres plantas recién florecidas por lote y para las raíces se realizó una microcalicata por planta hasta 30 cm de profundidad. Para la extracción nematológica en suelo se empleó el método de levigación de oostenbrink con embudo de Baermann y en raíz el método de licuado. Posteriormente, se cuantificó por género los especímenes encontrados. Resultados. Se encontró una población en el suelo (14 140 individuos) más significativa (p<0,05) que en la raíz (2670 individuos). Al comparar la población total por vigor no se observó diferencia estadística entre AV (395) y BV (305). Se determinaron doce géneros taxonómicos en las muestras de raíz y en suelo. Los especímenes más predominantes en AV fueron Helicotylenchus multicinctus y Radopholus similis y en los lotes BV fue Pratylenchus sp. Conclusión. Se observó una alta diversidad de la nematofauna presente tanto en los suelos como en raíces del cultivo