With 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.