Fast, Versatile and Quantitative Annotation of Complex Images

Bates, Kathleen; Shen, Jian; Chaudhary, Shivesh; Jackson-Holmes, Emily; Jue, Melinda L.; McCaskey, Erin; Goldman, Daniel I.; Lu, Hang

doi:10.2144/btn-2019-0010

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Most methods use simple morphological thinning or curvature of the perimeter (e.g. Leifer et al 2011;Yemini et al 2013), but problematic postures involving selfoverlap and collisions have been the focus of entire research papers [38,41], including one leading to a commercial software package (WormLab®), and another utilizing crowd sourcing [52]. Our ridgeline method has the advantage of not requiring the pruning of "split ends", but does fail in some cases.…”

Section: Discussionmentioning

confidence: 99%

Automated scoring of nematode nictation on a textured background

McClanahan

Golinelli

Lê

et al. 2023

Preprint

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Entomopathogenic nematodes including Steinernema spp. play an increasingly important role as biological alternatives to chemical pesticides. The infective juveniles of these worms use nictation - a behavior in which animals stand on their tails - as a host-seeking strategy. The developmentally-equivalent dauer larvae of the free-living nematode Caenorhabditis elegans also nictate, but as a means of phoresy or "hitching a ride" to a new food source. Advanced genetic and experimental tools have been developed for C. elegans, but time-consuming manual scoring of nictation slows efforts to understand this behavior, and the textured substrates required for nictation can frustrate traditional machine vision segmentation algorithms. Here we present a Mask R-CNN-based tracker capable of segmenting C. elegans dauers and S. carpocapsae infective juveniles on a textured background suitable for nictation, and a machine learning pipeline that scores nictation behavior. We use our system to show that the nictation propensity of C. elegans from high-density liquid cultures largely mirrors their development into dauers, and to quantify nictation in S. carpocapsae infective juveniles in the presence of a potential host. This system is an improvement upon existing intensity-based tracking algorithms and human scoring which can facilitate large-scale studies of nictation and potentially other nematode behaviors.

show abstract

Section: Discussionmentioning

confidence: 99%

Automated scoring of nematode nictation on a textured background

McClanahan

Golinelli

Lê

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Most methods use simple morphological thinning or curvature of the perimeter (e.g. Leifer et al 2011;Yemini et al 2013), but problematic postures involving self-overlap and collisions have been the focus of entire research papers [34,38], including one leading to a commercial software package (WormLab1), and another utilizing crowd sourcing [46]. Our ridgeline method has the advantage of not requiring the pruning of "split ends", but does fail in some cases.…”

Section: Discussionmentioning

confidence: 99%

Automated scoring of nematode nictation on a textured background

McClanahan,

Golinelli,

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Entomopathogenic nematodes, including Steinernema spp., play an increasingly important role as biological alternatives to chemical pesticides. The infective juveniles of these worms use nictation–a behavior in which animals stand on their tails–as a host-seeking strategy. The developmentally-equivalent dauer larvae of the free-living nematode Caenorhabditis elegans also nictate, but as a means of phoresy or "hitching a ride" to a new food source. Advanced genetic and experimental tools have been developed for C. elegans, but time-consuming manual scoring of nictation slows efforts to understand this behavior, and the textured substrates required for nictation can frustrate traditional machine vision segmentation algorithms. Here we present a Mask R-CNN-based tracker capable of segmenting C. elegans dauers and S. carpocapsae infective juveniles on a textured background suitable for nictation, and a machine learning pipeline that scores nictation behavior. We use our system to show that the nictation propensity of C. elegans from high-density liquid cultures largely mirrors their development into dauers, and to quantify nictation in S. carpocapsae infective juveniles in the presence of a potential host. This system is an improvement upon existing intensity-based tracking algorithms and human scoring which can facilitate large-scale studies of nictation and potentially other nematode behaviors.

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“…CellProfiler was published in 2006 [3] but the need for image analysis techniques is far from over. A recently published report in BioTechniques describes how the accelerating ease of collecting large image data sets has led to a shift in scientific bottlenecks, from image collection to image analysis, across many disciplines [4].…”

Section: The Big Picturementioning

confidence: 99%