A CNN Framework Based on Line Annotations for Detecting Nematodes in Microscopic Images

Chen, Long; Strauch, Martin; Daub, Matthias; Jiang, Xiaochen; Jansen, Marcus; Luigs, Hans-Georg; Schultz-Kuhlmann, Susanne; Krüssel, Stefan; Merhof, Dorit

doi:10.1109/isbi45749.2020.9098465

Cited by 22 publications

(21 citation statements)

References 13 publications

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“…This probability rises to 2.6%, 4.6%, and 6.6% with 30, 60, and 90 worms per plate, respectively, as proven in 25 . Therefore, classic methods are used with small worm numbers per plate (10,15 or 30 worms), where the probability of errors is low. However, the proposed method can work with bigger worm numbers per plate and does not diminish tracking performance.…”

Section: Discussionmentioning

confidence: 99%

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

Castro

Puchalt

Sánchez-Salmerón

2020

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

Castro

Puchalt

Sánchez-Salmerón

2020

Sci Rep

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“…Technical advances are being made and will deliver new techniques, for example detection algorithms that use remote sensing technologies in the field to delimit high-risk spots at early symptom development (see also Chapter 58, this volume) instead of sampling large areas. Future approaches like automatic detection and phenotyping of BCN in soil extracts using machine learning methods (Akintayo et al, 2018;Chen et al, 2020) will probably represent new key technologies for nematology that also potentially provide high throughput technologies. Technical development will enable faster and more complex calculation processing, which will be accompanied by the provision of complex models considering biotic, abiotic and technical patterns simultaneously for the provision of user-friendly decision support systems.…”

Section: Outlook: Anticipating Future Developmentsmentioning

confidence: 99%

Integrated Nematode Management

Solaimalai¹,

Anantharaju²,

Irulandi³

et al. 2020

Maize Crop

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“…A method that classifies different strains of C. elegans using convolutional neural networks (CNN) was presented in [ 17 ]. Methods based on neural networks have also been proposed for head and tail localisation [ 18 ] and pose estimation [ 19 , 20 , 21 ]. Recently, [ 22 , 23 ] used different convolutional neural network models to estimate the physiological age of C. elegans .…”

Section: Introductionmentioning

confidence: 99%

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

Garví

Puchalt

Castro

et al. 2021

Sensors

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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.

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A CNN Framework Based on Line Annotations for Detecting Nematodes in Microscopic Images

Cited by 22 publications

References 13 publications

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

Integrated Nematode Management

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

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