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      A Markerless Pose Estimator Applicable to Limbless Animals

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          Abstract

          The analysis of kinematics, locomotion, and spatial tasks relies on the accurate detection of animal positions and pose. Pose and position can be assessed with video analysis programs, the “trackers.” Most available trackers represent animals as single points in space (no pose information available) or use markers to build a skeletal representation of pose. Markers are either physical objects attached to the body (white balls, stickers, or paint) or they are defined in silico using recognizable body structures (e.g., joints, limbs, color patterns). Physical markers often cannot be used if the animals are small, lack prominent body structures on which the markers can be placed, or live in environments such as aquatic ones that might detach the marker. Here, we introduce a marker-free pose-estimator (LACE Limbless Animal tra Ck Er) that builds the pose of the animal de novo from its contour. LACE detects the contour of the animal and derives the body mid-line, building a pseudo-skeleton by defining vertices and edges. By applying LACE to analyse the pose of larval Drosophila melanogaster and adult zebrafish, we illustrate that LACE allows to quantify, for example, genetic alterations of peristaltic movements and gender-specific locomotion patterns that are associated with different body shapes. As illustrated by these examples, LACE provides a versatile method for assessing position, pose and movement patterns, even in animals without limbs.

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          Most cited references81

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

            Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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              A computational approach to edge detection.

              John Canny (1986)
              This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
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                Author and article information

                Contributors
                Journal
                Front Behav Neurosci
                Front Behav Neurosci
                Front. Behav. Neurosci.
                Frontiers in Behavioral Neuroscience
                Frontiers Media S.A.
                1662-5153
                28 March 2022
                2022
                : 16
                : 819146
                Affiliations
                [1] 1Department of Cellular Neuroscience, Georg-August-University Göttingen , Gottingen, Germany
                [2] 2Institute for Humangenetics, University Medical Center Göttingen, Georg-August-University Göttingen , Gottingen, Germany
                Author notes

                Edited by: Fabrizio Sanna, University of Cagliari, Italy

                Reviewed by: William Ryu, University of Toronto, Canada; Wolf Huetteroth, Leipzig University, Germany

                *Correspondence: Bart R. H. Geurten bgeurte@ 123456gwdg.de

                This article was submitted to Individual and Social Behaviors, a section of the journal Frontiers in Behavioral Neuroscience

                †Present address: Diego Giraldo, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins Malaria Research Institute, Johns Hopkins University, Baltimore, MD, United States

                Article
                10.3389/fnbeh.2022.819146
                8997243
                35418841
                02ec2cf7-6c5b-4528-be32-617127e47e64
                Copyright © 2022 Garg, André, Giraldo, Heyer, Göpfert, Dosch and Geurten.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 November 2021
                : 09 February 2022
                Page count
                Figures: 8, Tables: 1, Equations: 2, References: 82, Pages: 15, Words: 11419
                Funding
                Funded by: Deutsche Forschungsgemeinschaft, doi 10.13039/501100001659;
                Funded by: Deutscher Akademischer Austauschdienst, doi 10.13039/501100001655;
                Categories
                Behavioral Neuroscience
                Methods

                Neurosciences
                animal tracker,zebrafish,drosophila larva,gender dimorphism,hough transform,intermittant locomotion,saccades,undulatory swimming

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