Experimental digital Gabor hologram rendering of C. elegans worms by a model-trained convolutional neural network (Conference Presentation)

2020 ◽  
Author(s):  
Michael Atlan ◽  
Julie Rivet ◽  
Antoine Taliercio ◽  
Nicolas Boutry ◽  
Guillaume Tochon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
C Elegans

scholarly journals A tandem segmentation-classification approach for the localization of morphological predictors of C. elegans lifespan and motility

2021 ◽  
Author(s):  
Artur Yakimovich ◽  
Evgeniy Galimov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Posterior Part ◽  
Model Organism ◽  
Drug Effects ◽  
Basic Research ◽  
Classification Approach ◽  
Old Adults ◽  
C Elegans ◽  
Plat Form

C. elegans is an established model organism for studying genetic and drug effects on ageing, many of which are conserved in humans. It is also an important model for basic research, and C. elegans pathologies is a new emerging field. Here we develop a proof-of-principal convolutional neural network-based plat-form to segment C. elegans and extract features that might be useful for lifespan prediction. We use a dataset of 734 worms tracked throughout their lifespan and classify worms into long-lived and short-lived. We designed WormNet - a convolutional neural network (CNN) to predict the worm lifespan class based on young adult images (day 1 - day 3 old adults) and showed that WormNet, as well as, InceptionV3 CNN can successfully classify lifespan. Based on U-Net architecture we develop HydraNet CNNs which allow segmenting worms accurately into anterior, mid-body and posterior parts. We combine HydraNet segmentation, WormNet prediction and the class activation map approach to determine the segments most important for lifespan classification. Such a tandem segmentation-classification approach shows posterior part of the worm might be more important for classifying long-lived worms. Our approach can be useful for the acceleration of anti-ageing drug discovery and for studying C. elegans pathologies.

scholarly journals Identification of C. elegans strains using a fully convolutional neural network on behavioural dynamics

2018 ◽  
Author(s):  
Avelino Javer ◽  
André E.X. Brown ◽  
Iasonas Kokkinos ◽  
Jens Rittscher
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Spontaneous Activity ◽  
Genetic Basis ◽  
Model Organism ◽  
C Elegans ◽  
Deep Learning Model ◽  
Behavioural Dynamics

AbstractThe nematode C. elegans is a promising model organism to understand the genetic basis of behaviour due to its anatomical simplicity. In this work, we present a deep learning model capable of discerning genetically diverse strains based only on their recorded spontaneous activity, and explore how its performance changes as different embeddings are used as input. The model outperforms hand-crafted features on strain classification when trained directly on time series of worm postures.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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