Development and Evaluation of New Methods for Automating Experiments with C. Elegans Based on Active Vision

Tesis por compendio[ES] Esta tesis se centra en el desarrollo de nuevas técnicas automatizadas que permiten inspeccionar nematodos Caenorhabidits elegans (C. elegans) en placas de Petri estándar, para el análisis de sus comportamientos. C. elegans es un nemátodo de 1mm de longitud, con el cual se pueden realizar distintos experimentos para analizar los efectos de fármacos, compuestos o alteraciones genéticas en su longevidad, su salud física o su cognición. El campo principal metodológico del presente trabajo para el análisis de esos efectos es la visión por computador; y con ello, el desarrollo completo del sistema de visión activo: sistema de iluminación inteligente, sistema de captura óptimo, procesamiento de las imágenes para detección y clasificación de nematodos. Los campos secundarios en esta investigación son el control y robotización. Los C. elegans son animales sensibles a la luz y por ello el primero de los métodos está en la rama de la iluminación inteligente, con el cual se permite regular la intensidad y las longitudes de onda de la luz que reciben los nematodos. El siguiente método es el procesado para la detección y clasificación de movimiento a partir de las imágenes obtenidas con esa iluminación controlada. Tener el ambiente controlado es fundamental, los nematodos son muy sensibles a las condiciones ambientales por lo que puede alterarse su actividad biológica, y con ello los resultados, así que el tercer método es la integración de las técnicas en un nuevo dispositivo que permite automatizar ensayos de lifespan y validar los resultados automáticos comparándolos con los manuales. El movimiento del animal es clave para poder realizar inferencias estadísticas que puedan mostrar tendencias en sus comportamientos, por ello la estimulación automatizada que provoque una reacción de su movilidad es el cuarto de los métodos. Por último, el aumento de la resolución en las imágenes muestra mayor detalle, mejorando el procesamiento y extracción de características. El quinto método es un robot multivista que posibilita tomar imágenes a distintas resoluciones, lo que permite mantener el seguimiento global de los gusanos, al mismo tiempo que se toman imágenes con un encuadre de mayor detalle del nematodo objetivo.[CA] Esta tesi doctoral se centra en el desentrollament de noves tècniques automatitzades que permeten inspeccionar nemàtodes Caenorhabidits elegans (C. elegans) en plaques de Petri estàndar, per a l'anàlisi dels seus comportaments. C. elegans és un nemàtode d'1mm de llargària, ab el qual se poden realitzar distints experiments per a analitzar els efectes de fàrmacs, composts o alteracions genètiques en sa longevitat, la seua salut física o la seua cognició. El camp principal metodològic del present treball per a l'anàlisi d'eixos efectes és la visió per computador; i ab açò, el desentrollament complet del sistema de visió actiu: sistema d'il.luminació inteligent, sistema de captura òptim, processament de les imàtgens per a detecció i classificació de nematode. Els camps secundaris en esta investigació són el control i robotització. Els C. elegans són animals sensibles a la llum i por ello el primer dels mètodes està en la branca de la il.luminació intel.ligent, ab el qual es permet regular la intensitat i les longituds d'ona de la llum que reben els nematodes. El següent mètode és el processat per a la detecció i classificació de moviment a partir de les imàtgens obtinguda ab eixa il.luminació controlada. Tindre l'ambient controlat és fonamental, els nemàtodes són molt sensibles a les condicions ambientals per lo que pot alterar-se la seua activitat biològica, i ab aço els resultats, aixina que el tercer mètode és la integració de les tècniques en un nou dispositiu que permet automatitzar ensajos de lifespan i validar els resultats automàtics comparant-los ab els manuals. El moviment de l'animal és clau per a poder realitzar inferencies estadístiques que puguen mostrar tendències en el seus comportaments, per això la estimulació automatitzada que provoque una reacció de la seua mobilitat és el quart dels mètodes. Per últim, l'augment de la resolució en les imàtgens mostra major detall, millorant el processament i extracció de característiques. El quint mètode és un robot multivista que possibilita prendre imàtgens a distintes resolucions, lo que permet mantindre el seguiment global dels cucs, al mateix temps que se prenguen imàtgens ab un enquadrament de major detall del nematode objectiu.[EN] This thesis focuses on the development of new automated techniques that allow the inspection of Caenorhabidits elegans nematodes (C. elegans) in Petri dishes, for the analysis of their behavior. This nematode is a 1mm long worm, with which different experiments can be carried out to analyze the effects of drugs, compounds or genetic alterations on its longevity, physical health or cognition. The main methodological field of the present work for the analysis of these effects is computer vision; and with it, the complete development of the active vision system: intelligent lighting system, optimal capture system, image processing for detection and classification of nematodes. The secondary fields in this research are control and robotization. C. elegans are light-sensitive animals and therefore the first method is in the field of intelligent lighting, with which it is possible to regulate the intensity and wavelength of the light that nematodes receive. The next method is the processing for the detection and classification of movement from the images obtained with that controlled lighting. Having a controlled environment is essential, worms are very sensitive to environmental conditions so it can alter biological activity, and with it the results, so the third method is the integration of techniques in a new device that allows automating tests of lifespan and validate the automatic results comparing them with the manual ones. The movement of the animal is key to be able to carry out statistical conferences that can show trends in its behaviors, therefore the automated stimulation that causes a reaction of its mobility is the fourth of the methods. Finally, increasing the resolution in the images shows greater detail, improving the processing and extraction of features. The fifth method is a multiview robot that enables images to be taken at different resolutions, allowing global tracking of worms to be maintained, while at the same time taking images with a more detailed frame of the target worm.Puchalt Rodríguez, JC. (2022). Development and Evaluation of New Methods for Automating Experiments with C. Elegans Based on Active Vision [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181359Compendi

Reducing Results Variance in Lifespan Machines: An Analysis of the Influence of Vibrotaxis on Wild-Type Caenorhabditis elegans for the Death Criterion

[EN] Nowadays, various artificial vision-based machines automate the lifespan assays of C. elegans. These automated machines present wider variability in results than manual assays because in the latter worms can be poked one by one to determine whether they are alive or not. Lifespan machines normally use a "dead or alive criterion" based on nematode position or pose changes, without poking worms. However, worms barely move on their last days of life, even though they are still alive. Therefore, a long monitoring period is necessary to observe motility in order to guarantee worms are actually dead, or a stimulus to prompt worm movement is required to reduce the lifespan variability measure. Here, a new automated vibrotaxis-based method for lifespan machines is proposed as a solution to prompt a motion response in all worms cultured on standard Petri plates in order to better distinguish between live and dead individuals. This simple automated method allows the stimulation of all animals through the whole plate at the same time and intensity, increasing the experiment throughput. The experimental results exhibited improved live-worm detection using this method, and most live nematodes (>93%) reacted to the vibration stimulus. This method increased machine sensitivity by decreasing results variance by approximately one half (from +/- 1 individual error per plate to +/- 0.6) and error in lifespan curve was reduced as well (from 2.6% to 1.2%).This study was also supported by the Universitat Politecnica de Valencia with Project 20170020-UPV, Plan Nacional de I+D with Project RTI2018-094312-B-I00 and by European FEDER funds. ADM Nutrition, Biopolis SL and Archer Daniels Midland provided support in the supply of C. elegans.Puchalt-Rodríguez, JC.; Layana-Castro, PE.; Sánchez Salmerón, AJ. (2020). Reducing Results Variance in Lifespan Machines: An Analysis of the Influence of Vibrotaxis on Wild-Type Caenorhabditis elegans for the Death Criterion. Sensors. 20(21):1-17. https://doi.org/10.3390/s20215981S117202

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

[EN] One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.This study was supported by the Plan Nacional de I+D with Project RTI2018-094312-B-I00 and by European FEDER funds. ADM Nutrition, Biopolis S.L. and Archer Daniels Midland supplied the C. elegans plates. Some strains were provided by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). Mrs. Maria-Gabriela Salazar-Secada developed the skeleton annotation application. Mr. Jordi Tortosa-Grau annotated worm skeletons.Layana-Castro, PE.; Puchalt-Rodríguez, JC.; Sánchez Salmerón, AJ. (2020). Improving skeleton algorithm for helping Caenorhabditis elegans trackers. Scientific Reports. 10(1):1-12. https://doi.org/10.1038/s41598-020-79430-8S112101Teo, E. et al. 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Machine vision detection of isolated and overlapped nematode worms using skeleton analysis. In 2008 15th IEEE International Conference on Image Processing, 2972–2975. https://doi.org/10.1109/ICIP.2008.4712419 (2008).Uhlmann, V. & Unser, M. Tip-seeking active contours for bioimage segmentation. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 544–547 (2015).Nagy, S., Goessling, M., Amit, Y. & Biron, D. A generative statistical algorithm for automatic detection of complex postures. PLOS Comput. Biol. 11, 1–23. https://doi.org/10.1371/journal.pcbi.1004517 (2015).Huang, K.-M., Cosman, P. & Schafer, W. R. Machine vision based detection of omega bends and reversals in C. elegans. J. Neurosci. Methods 158, 323–336. https://doi.org/10.1016/j.jneumeth.2006.06.007 (2006).Kiel, M. et al. A multi-purpose worm tracker based on FIM. https://doi.org/10.1101/352948 (2018).Winter, P. B. et al. 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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

Improving lifespan automation for Caenorhabditis elegans by using image processing and a post-processing adaptive data filter

[EN] Automated lifespan determination for C. elegans cultured in standard Petri dishes is challenging. Problems include occlusions of Petri dish edges, aggregation of worms, and accumulation of dirt (dust spots on lids) during assays, etc. This work presents a protocol for a lifespan assay, with two image-processing pipelines applied to different plate zones, and a new data post-processing method to solve the aforementioned problems. Specifically, certain steps in the culture protocol were taken to alleviate aggregation, occlusions, contamination, and condensation problems. This method is based on an active illumination system and facilitates automated image sequence analysis, does not need human threshold adjustments, and simplifies the techniques required to extract lifespan curves. In addition, two image-processing pipelines, applied to different plate zones, were employed for automated lifespan determination. The first image-processing pipeline was applied to a wall zone and used only pixel level information because worm size or shape features were unavailable in this zone. However, the second image-processing pipeline, applied to the plate centre, fused information at worm and pixel levels. Simple death event detection was used to automatically obtain lifespan curves from the image sequences that were captured once daily throughout the assay. Finally, a new post-processing method was applied to the extracted lifespan curves to filter errors. The experimental results showed that the errors in automated counting of live worms followed the Gaussian distribution with a mean of 2.91% and a standard deviation of +/- 12.73% per Petri plate. Post-processing reduced this error to 0.54 +/- 8.18% per plate. The automated survival curve incurred an error of 4.62 +/- 2.01%, while the post-process method reduced the lifespan curve error to approximately 2.24 +/- 0.55%.This study was also supported by the CDTI agency of the Spanish Ministry of Economy and Competitiveness with CIEN project SMARTFOODS, Universitat PolitAcnica de Valencia with Project 20170020-UPV, Plan Nacional de I + D with Project RTI2018-094312-B-I00 and by European FEDER funds. ADM Nutrition, Biopolis SL and Archer Daniels Midland provided support in the form of salaries for authors P. M. Guerola and S. G. Martinez.Puchalt-Rodríguez, JC.; Sánchez Salmerón, AJ.; Ivorra Martínez, E.; Genovés Martínez, S.; Martínez, R.; Martorell Guerola, P. (2020). Improving lifespan automation for Caenorhabditis elegans by using image processing and a post-processing adaptive data filter. Scientific Reports. 10(1):1-14. https://doi.org/10.1038/s41598-020-65619-4114101Brenner, S. The Genetics Of Caenorhabditis Elegans. 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Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard Petri dishes

[EN] Data from manual healthspan assays of the nematode Caenorhabditis elegans (C. elegans) can be complex to quantify. The first attempts to quantify motor performance were done manually, using the so-called thrashing or body bends assay. Some laboratories have automated these approaches using methods that help substantially to quantify these characteristic movements in small well plates. Even so, it is sometimes difficult to find differences in motor behaviour between strains, and/or between treated vs untreated worms. For this reason, we present here a new automated method that increases the resolution flexibility, in order to capture more movement details in large standard Petri dishes, in such way that those movements are less restricted. This method is based on a Cartesian robot, which enables high-resolution images capture in standard Petri dishes. Several cameras mounted strategically on the robot and working with different fields of view, capture the required C. elegans visual information. We have performed a locomotion-based healthspan experiment with several mutant strains, and we have been able to detect statistically significant differences between two strains that show very similar movement patterns.This work was supported by the research agency of the Spanish Ministry of Science and Innovation under Grant RTI2018-094312-B-I00 (European FEDER funds).Puchalt-Rodríguez, JC.; González-Rojo, JF.; Gómez-Escribano, AP.; Vázquez-Manrique, RP.; Sánchez Salmerón, AJ. (2022). Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard Petri dishes. Scientific Reports. 12(1):1-11. https://doi.org/10.1038/s41598-022-05823-611112

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