000195775 001__ 195775
000195775 005__ 20190316235826.0
000195775 0247_ $$2doi$$a10.1109/Tbme.2014.2298612
000195775 02470 $$2ISI$$a000340259900006
000195775 037__ $$aARTICLE
000195775 245__ $$aCaenorhabditis elegant segmentation using texture-based models for motility phenotyping
000195775 269__ $$a2014
000195775 260__ $$bIeee-Inst Electrical Electronics Engineers Inc$$c2014$$aPiscataway
000195775 300__ $$a12
000195775 336__ $$aJournal Articles
000195775 520__ $$aWith widening interests in using model organisms for reverse genetic approaches and biomimmetic micro-robotics, motility phenotyping of the nematode Caenorhabditis elegans is expanding across a growing array of locomotive environments. One ongoing bottleneck lies in providing users with automatic ne- matode segmentations of C. elegans in image sequences featuring complex and dynamic visual cues, a first and necessary step prior to extracting motility phenotypes. Here, we propose to tackle such automatic segmentation challenges by introducing a novel Texture Feature Model (TFM). Our approach revolves around the use of combined intensity- and texture-based features integrated within a probabilistic framework. This strategy first provides a coarse nematode segmentation from which a Markov Random Field (MRF) model is used to refine the segmentation by inferring pixels belonging to the nematode using an approximate inference technique. Finally, informative priors can then be estimated and integrated in our framework to provide coherent segmentations across image sequences. We validate our TFM method across a wide range of motility environments. Not only does TFM assure comparative performances to existing segmentation methods on traditional environments featuring static backgrounds, it importantly provides state-of-the-art C. elegans segmentations for dynamic environments such as the recently introduced wet granular media. We show how such segmentations may be used to compute nematode “skeletons” from which motility phenotypes can then be extracted. Overall, our TFM method provides users with a tangible solution to tackle the growing needs of C. elegans segmentation in challenging motility environments.
000195775 6531_ $$aComputer Vision
000195775 6531_ $$aImage Segmentation
000195775 6531_ $$aC. Elegans phenotyping
000195775 700__ $$0245861$$g177109$$aSznitman, Raphael
000195775 700__ $$aGreenblum, Ayala
000195775 700__ $$0240252$$g112366$$aFua, Pascal
000195775 700__ $$aArratia, Paulo
000195775 700__ $$aSznitman, Josue
000195775 773__ $$j61$$tIEEE Transactions on Biomedical Engineering$$k8$$q2278-2289
000195775 8564_ $$uhttps://infoscience.epfl.ch/record/195775/files/IEEE_FinalSubmissionFile.pdf$$zPreprint$$s18274891$$yPreprint
000195775 909C0 $$xU10659$$0252087$$pCVLAB
000195775 909CO $$qGLOBAL_SET$$pIC$$ooai:infoscience.tind.io:195775$$particle
000195775 917Z8 $$x177109
000195775 917Z8 $$x148230
000195775 937__ $$aEPFL-ARTICLE-195775
000195775 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000195775 980__ $$aARTICLE