Diseño, desarrollo y evaluación de algoritmos basados en aprendizaje profundo para automatización de experimentos Lifespan con C. elegans

[ES] En los últimos años, los nematodos C. elegans cultivados en placas de Petri se han utilizado en muchas investigaciones relacionadas con el envejecimiento. El desarrollo de nuevas herramientas para automatizar los experimentos de lifespan permite realizar más ensayos en menos tiempo y evitar errores humanos, obteniendo resultados más precisos. El objetivo de este TFM consiste en diseñar y desarrollar métodos para abordar este problema utilizando técnicas de aprendizaje profundo. Posteriormente, se evaluarán los resultados comparando los resultados con los obtenidos empleando técnicas tradicionales de visión por computador. Inicialmente, el trabajo se centrará en la creación y edición de forma supervisada de un conjunto de imágenes bien etiquetadas. Posteriormente se diseñarán distintas arquitecturas de redes neuronales y se optimizará cada una de ellas sobre el espacio de hiperparámetros utilizando Python y Pytorch. Finalmente, se evaluarán las distintas arquitecturas propuestas, utilizando como criterios de optimización tanto las tasas de aciertos como los costes temporales de computación.[EN] In recent years, C. elegans nematodes grown in Petri dishes have been used in many investigations related to aging. The development of new tools to automate lifespan experiments allows more tests to be carried out in less time and to avoid human error, obtaining more accurate results. The objective of this TFM is to design and develop methods to address this problem using deep learning techniques. Subsequently, the results will be evaluated by comparing the results with those obtained using traditional computer vision techniques. Initially, work will focus on supervised creation and editing of a set of well-labeled images. Subsequently, different neural network architectures will be designed and each one will be optimized on the hyperparameter space using Python and Pytorch. Finally, the different proposed architectures will be evaluated, using both the accuracies and the temporary computing costs as optimization criteria.[CA] En els últims anys, els nematodes C. elegans conreats en plaques de Petri s'han utilitzat en moltes recerques relacionades amb l'envelliment. El desenvolupament de noves eines per a automatitzar els experiments de lifespan permet realitzar més assajos en menys temps i evitar errors humans, obtenint resultats més precisos. L'objectiu d'aquest TFM consisteix a dissenyar i desenvolupar mètodes per a abordar aquest problema utilitzant tècniques d'aprenentatge profund. Posteriorment, s'avaluaran els resultats comparant els resultats amb els obtinguts emprant tècniques tradicionals de visió per computador. Inicialment, el treball se centrarà en la creació i edició de forma supervisada d'un conjunt d'imatges ben etiquetades. Posteriorment es dissenyaran diferents arquitectures de xarxes neuronals i s'optimitzarà cadascuna d'elles sobre l'espai de hiperparámetros utilitzant Python i Pytorch. Finalment, s'avaluaran les diferents arquitectures proposades, utilitzant com a criteris d'optimització tant les taxes d'encerts com els costos temporals de computació.García Garví, A. (2020). Diseño, desarrollo y evaluación de algoritmos basados en aprendizaje profundo para automatización de experimentos Lifespan con C. elegans. http://hdl.handle.net/10251/151938TFG

Diseño, desarrollo y evaluación de un sistema de clasificación de objetos en imágenes que permita la monitorización de C. elegans mediante redes neuronales convolucionales

[ES] En este proyecto se ha desarrollado un algoritmo que facilita las tareas de monitorización de nematodos Caenorhabditis elegans (C. elegans), con el objetivo de automatizar las tareas de investigación. En concreto, se trata de un clasificador de imágenes que permite detectar si el gusano está vivo o muerto. En primer lugar, se ha realizado un estudio de alternativas revisando proyectos similares y comparando las técnicas tradicionales de visión por computador y los algoritmos de Aprendizaje Profundo (Deep Learning). Tras analizar sus ventajas y desventajas, se ha optado por hacer uso de redes neuronales artificiales. Para poder entrenar redes neuronales, es necesario disponer de una gran cantidad de imágenes etiquetadas, por lo que se ha procedido a etiquetar la base de datos disponible. Seguidamente, se ha planteado el uso de dos arquitecturas de redes neuronales: las redes neuronales convolucionales y las redes neuronales recurrentes. Para cada tipo de red se necesita un tipo de entrada, por lo que se han propuesto varios métodos para introducir la información. Una vez generado el dataset completo, se ha divido en grupos de entrenamiento y validación. A continuación, se han implementado las redes neuronales haciendo uso del software de Deep Learning Pytorch y se han desarrollado diferentes pruebas para determinar la arquitectura más apropiada para resolver el problema y optimizar los resultados. Como conclusión, se ha comprobado que la combinación de redes neuronales convolucionales y recurrentes obtiene los mejores porcentajes de precisión al clasificar las imágenes de C. elegans.[EN] In this project an algorithm has been developed that facilitates the monitoring tasks of nematodes Caenorhabditis elegans (C. elegans), with the aim of automating research tasks. Specifically, it is an image classifier that allows to detect if the worm is alive or dead. First of all, a study of alternatives has been carried out by reviewing similar projects and comparing traditional computer vision techniques and Deep Learning algorithms. After analyzing their advantages and disadvantages, it has been decided to make use of artificial neural networks. In order to train neural networks, it is necessary to have a large number of tagged images, so we have proceeded to tag the database available. Next, the use of two neural network architectures has been considered: convolutional neural networks and recurrent neural networks. For each type of network an input type is needed, so several methods have been proposed to introduce the information. Once the complete dataset has been generated, it has been divided into training and validation groups. Next, neural networks have been implemented using the Deep Learning Pytorch software and different tests have been developed to determine the most appropriate architecture to solve the problem and optimize the results. As a conclusion, it has been proved that the combination of convolutional and recurrent neural networks obtains the best percentages of precision when classifying the images of C. elegans.García Garví, A. (2019). Diseño, desarrollo y evaluación de un sistema de clasificación de objetos en imágenes que permita la monitorización de C. elegans mediante redes neuronales convolucionales. Universitat Politècnica de València. http://hdl.handle.net/10251/12630

Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification

[EN] The automation of lifespan assays with C. elegans in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or dead can be complex as they barely move during the last few days of their lives. This paper proposes a method combining traditional computer vision techniques with a live/dead C. elegans classifier based on convolutional and recurrent neural networks from low-resolution image sequences. In addition to proposing a new method to automate lifespan, the use of data augmentation techniques is proposed to train the network in the absence of large numbers of samples. The proposed method achieved small error rates (3.54% +/- 1.30% per plate) with respect to the manual curve, demonstrating its feasibility.This study was supported by the Plan Nacional de I + D under the project RTI2018-094312B-I00 and by the European FEDER funds.García-Garví, A.; Puchalt-Rodríguez, JC.; Layana-Castro, PE.; Navarro Moya, F.; Sánchez Salmerón, AJ. (2021). Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification. Sensors. 21(14):1-17. https://doi.org/10.3390/s21144943117211

Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation

In recent decades, assays with the nematode Caenorhabditis elegans (C. elegans) have enabled great advances to be made in research on aging. However, performing these assays manually is a laborious task. To solve this problem, numerous C. elegans assay automation techniques are being developed to increase throughput and accuracy. In this paper, a method for predicting the lifespan of C. elegans nematodes using a bimodal neural network is proposed and analyzed. Specifically, the model uses the sequence of images and the count of live C. elegans up to the current day to predict the lifespan curve termination. This network has been trained using a simulator to avoid the labeling costs of training such a model. In addition, a method for estimating the uncertainty of the model predictions has been proposed. Using this uncertainty, a criterion has been analyzed to decide at what point the assay could be halted and the user could rely on the model’s predictions. The method has been analyzed and validated using real experiments. The results show that uncertainty is reduced from the mean lifespan and that most of the predictions obtained do not present statistically significant differences with respect to the curves obtained manually

Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences

Pose estimation of C. elegans in image sequences is challenging and even more difficult in low-resolution images. Problems range from occlusions, loss of worm identity, and overlaps to aggregations that are too complex or difficult to resolve, even for the human eye. Neural networks, on the other hand, have shown good results in both low-resolution and high-resolution images. However, training in a neural network model requires a very large and balanced dataset, which is sometimes impossible or too expensive to obtain.In this article, a novel method for predicting C. elegans poses in cases of multi-worm aggregation and aggregation with noise is proposed. To solve this problem we use an improved U-Net model capable of obtaining images of the next aggregated worm posture. This neural network model was trained/validated using a custom-generated dataset with a synthetic image simulator. Subsequently, tested with a dataset of real images. The results obtained were greater than 75% in precision and 0.65 with Intersection over Union (IoU) values

Automation of Caenorhabditis elegans lifespan assay using a simplified domain synthetic image-based neural network training strategy

Performing lifespan assays with Caenorhabditis elegans (C. elegans) nematodes manually is a time consuming and laborious task. Therefore, automation is necessary to increase productivity. In this paper, we propose a method to automate the counting of live C. elegans using deep learning. The survival curves of the experiment are obtained using a sequence formed by an image taken on each day of the assay. Solving this problem would require a very large labeled dataset; thus, to facilitate its generation, we propose a simplified image-based strategy. This simplification consists of transforming the real images of the nematodes in the Petri dish to a synthetic image, in which circular blobs are drawn on a constant background to mark the position of the C. elegans. To apply this simplification method, it is divided into two steps. First, a Faster R-CNN network detects the C. elegans, allowing its transformation into a synthetic image. Second, using the simplified image sequence as input, a regression neural network is in charge of predicting the count of live nematodes on each day of the experiment. In this way, the counting network was trained using a simple simulator, avoiding labeling a very large real dataset or developing a realistic simulator. Results showed that the differences between the curves obtained by the proposed method and the manual curves are not statistically significant for either short-lived N2 (p-value log rank test 0.45) or long-lived daf-2 (p-value log rank test 0.83) strains

Towards generalization for Caenorhabditis elegans detection

The nematode Caenorhabditis elegans (C. elegans) is of significant interest for research into neurodegenerative diseases, aging, and drug screening. However, conducting these assays manually is a tedious and time-consuming process. This paper proposes a methodology to achieve a generalist C. elegans detection algorithm, as previous work only focused on dataset-specific detection, tailored exclusively to the characteristics and appearance of the images in a given dataset. The main aim of our study is to achieve a solution that allows for robust detection, regardless of the image-capture system used, with the potential to serve as a basis for the automation of numerous assays. These potential applications include worm counting, worm tracking, motion detection and motion characterization. To train this model, a dataset consisting of a wide variety of appearances adopted by C. elegans has been curated and dataset augmentation methods have been proposed and evaluated, including synthetic image generation. The results show that the model achieves an average precision of 89.5% for a wide variety of C. elegans appearances that were not used during training, thereby validating its generalization capabilities

Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm

[EN] Automatic tracking of Caenorhabditis elegans (C. egans) in standard Petri dishes is challenging due to high-resolution image requirements when fully monitoring a Petri dish, but mainly due to potential losses of individual worm identity caused by aggregation of worms, overlaps and body contact. To date, trackers only automate tests for individual worm behaviors, canceling data when body contact occurs. However, essays automating contact behaviors still require solutions to this problem. In this work, we propose a solution to this difficulty using computer vision techniques. On the one hand, a skeletonization method is applied to extract skeletons in overlap and contact situations. On the other hand, new optimization methods are proposed to solve the identity problem during these situations. Experiments were performed with 70 tracks and 3779 poses (skeletons) of C. elegans. Several cost functions with different criteria have been evaluated, and the best results gave an accuracy of 99.42% in overlapping with other worms and noise on the plate using the modified skeleton algorithm and 98.73% precision using the classical skeleton algorithmThis study was supported by the Plan Nacional de I+D with Project RTI2018-094312-B-I00, FPI Predoctoral contract PRE2019-088214 and by European FEDER funds.Layana-Castro, PE.; Puchalt-Rodríguez, JC.; García-Garví, A.; Sánchez Salmerón, AJ. (2021). Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm. Sensors. 21(16):1-21. https://doi.org/10.3390/s21165622121211

Documento orientativo de especificaciones de los sistemas de autocontrol. 3ª ed

YesEn el ámbito de la seguridad alimentaria, el Sistema de Autocontrol se ha convertido en una herramienta verdaderamente eficaz para asegurar la inocuidad y salubridad de los alimentos. El sistema de Autocontrol está basado en una metodología que ha acabado por imponerse, no sólo por su utilidad científica y técnica, sino por ser hoy una exigencia legal, en el ámbito europeo, y prácticamente una condición previa en el comercio mundial de alimentos. Este documento recoge los últimos avances en los temas de autocontrol con la intención de servir de guía a los responsables de las empresas alimentarias para que pueda ser adaptado a las necesidades de la empresa y como instrumento de trabajo a todos los que participan en el Control Sanitario Oficial de alimentos