WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
An important model system for understanding genes, neurons and behavior, the nematode worm C . elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C . elega...
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| Published in | PLoS computational biology Vol. 17; no. 4; p. e1008914 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.04.2021
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1553-7358 1553-734X 1553-7358 |
| DOI | 10.1371/journal.pcbi.1008914 |
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| Abstract | An important model system for understanding genes, neurons and behavior, the nematode worm
C
.
elegans
naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in
C
.
elegans
, including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (∼ 8 hour), fast-sampled (∼ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors. |
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| AbstractList | An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C. elegans, including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (∼ 8 hour), fast-sampled (∼ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors. An important model system for understanding genes, neurons and behavior, the nematode worm C . elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C . elegans , including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (∼ 8 hour), fast-sampled (∼ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors. Introduction All animals, including humans, reveal important and subtle information about their internal dynamics in their outward configurations of body posture, whether these internal dynamics originate from gene expression [1], neural activity [2], or motor control strategies [3]. The nematode C. elegans naturally exhibits a variety of coiled shapes which challenge the determination of the centerline posture, a fundamental component for quantitative behavioral understanding. The correct centerline (red) can be determined by close visual inspection (A), however high-throughput analysis necessitates a pose estimation algorithm which is robust to fluctuations in brightness, blur, noise, and occlusion. https://doi.org/10.1371/journal.pcbi.1008914.g001 Classical image skeletonization methods can be used to identify the worm centerline for non-overlapping shapes [7] and are employed in widely-used worm trackers because of their simplicity and speed. Another recent technique utilizes an optimization algorithm by searching for image matches in the “eigenworm” posture space [9], but is limited in efficacy by the slow nature of multi-dimensional image search and by the low resolving power of a comparison metric, which uses only a binary version of the raw image. An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C. elegans, including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (∼ 8 hour), fast-sampled (∼ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors. Recent advances in machine learning have enabled the high-resolution estimation of bodypoint positions of freely behaving animals, but manual labeling can render these methods imprecise and impractical, especially in highly deformable animals such as the nematode C. elegans. Such animals also frequently coil, resulting in complicated shapes whose ambiguity presents difficulties for standard pose estimation methods. Efficiently solving coiled shapes in C. elegans, exhibited in a variety of important natural contexts, is the primary limiting factor for fully automated high-throughput behavior analysis. WormPose provides pose estimation that works across imaging conditions, naturally complements existing worm trackers, and harnesses the power of deep convolutional networks but with an image generator to automatically provide precise image-centerline pairings for training. We apply WormPose to on-food recordings, finding a near absence of deep δ-turns. We also show that incoherent body motions in the dwell state, which do not translate the worm, have been misidentified as an increase in reversal rate by previous, centroid-based methods. We expect that the combination of a body model and image synthesis demonstrated in WormPose will be both of general interest and important for future progress in precise pose estimation in other slender-bodied and deformable organisms. An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C. elegans, including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (~ 8 hour), fast-sampled (~ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors. An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C. elegans, including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (∼ 8 hour), fast-sampled (∼ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors.An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C. elegans, including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (∼ 8 hour), fast-sampled (∼ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors. Introduction All animals, including humans, reveal important and subtle information about their internal dynamics in their outward configurations of body posture, whether these internal dynamics originate from gene expression [1], neural activity [2], or motor control strategies [3]. The nematode C. elegans naturally exhibits a variety of coiled shapes which challenge the determination of the centerline posture, a fundamental component for quantitative behavioral understanding. The correct centerline (red) can be determined by close visual inspection (A), however high-throughput analysis necessitates a pose estimation algorithm which is robust to fluctuations in brightness, blur, noise, and occlusion. https://doi.org/10.1371/journal.pcbi.1008914.g001 Classical image skeletonization methods can be used to identify the worm centerline for non-overlapping shapes [7] and are employed in widely-used worm trackers because of their simplicity and speed. Another recent technique utilizes an optimization algorithm by searching for image matches in the “eigenworm” posture space [9], but is limited in efficacy by the slow nature of multi-dimensional image search and by the low resolving power of a comparison metric, which uses only a binary version of the raw image. |
| Audience | Academic |
| Author | Hebert, Laetitia O’Shaughnessy, Liam Ahamed, Tosif Costa, Antonio C. Stephens, Greg J. |
| AuthorAffiliation | 2 Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, Canada 3 Department of Physics & Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands Hebrew University of Jerusalem, ISRAEL 1 Biological Physics Theory Unit, OIST Graduate University, Onna, Japan |
| AuthorAffiliation_xml | – name: 3 Department of Physics & Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands – name: 1 Biological Physics Theory Unit, OIST Graduate University, Onna, Japan – name: 2 Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, Canada – name: Hebrew University of Jerusalem, ISRAEL |
| Author_xml | – sequence: 1 givenname: Laetitia orcidid: 0000-0002-9699-0082 surname: Hebert fullname: Hebert, Laetitia – sequence: 2 givenname: Tosif orcidid: 0000-0002-1405-3539 surname: Ahamed fullname: Ahamed, Tosif – sequence: 3 givenname: Antonio C. orcidid: 0000-0002-7491-9250 surname: Costa fullname: Costa, Antonio C. – sequence: 4 givenname: Liam orcidid: 0000-0002-2260-2569 surname: O’Shaughnessy fullname: O’Shaughnessy, Liam – sequence: 5 givenname: Greg J. orcidid: 0000-0003-3135-3514 surname: Stephens fullname: Stephens, Greg J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33905413$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Hebert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Hebert et al 2021 Hebert et al |
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| Snippet | An important model system for understanding genes, neurons and behavior, the nematode worm
C
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naturally moves through a variety of complex postures,... An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures,... Introduction All animals, including humans, reveal important and subtle information about their internal dynamics in their outward configurations of body... |
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| Title | WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans |
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