Dendritic tree extraction from noisy maximum intensity projection images in C. elegans

Background: Maximum Intensity Projections (MIP) of neuronal dendritic trees obtained from confocal microscopy are frequently used to study the relationship between tree morphology and mechanosensory function in the model organism C. elegans. Extracting dendritic trees from noisy images remains however a strenuous process that has traditionally relied on manual approaches. Here, we focus on automated and reliable 2D segmentations of dendritic trees following a statistical learning framework. Methods: Our dendritic tree extraction (DTE) method uses small amounts of labelled training data on MIPs to learn noise models of texture-based features from the responses of tree structures and image background. Our strategy lies in evaluating statistical models of noise that account for both the variability generated from the imaging process and from the aggregation of information in the MIP images. These noisy models are then used within a probabilistic, or Bayesian framework to provide a coarse 2D dendritic tree segmentation. Finally, some post-processing is applied to refine the segmentations and provide skeletonized trees using a morphological thinning process. Results: Following a Leave-One-Out Cross Validation (LOOCV) method for an MIP database with available “ground truth” images, we demonstrate that our approach provides significant improvements in tree-structure segmentations over traditional intensity-based methods. Improvements for MIPs under various imaging conditions are both qualitative and quantitative, as measured from Receiver Operator Characteristic (ROC) curves and the yield and error rates in the final segmentations. In a final step, we demonstrate our DTE approach on previously unseen MIP samples including the extraction of skeletonized structures, and compare our method to a state-of-the art dendritic tree tracing software. Conclusions: Overall, our DTE method allows for robust dendritic tree segmentations in noisy MIPs, outperforming traditional intensity-based methods. Such approach provides a useable segmentation framework, ultimately delivering a speed-up for dendritic tree identification on the user end and a reliable first step towards further morphological characterizations of tree arborization

Caenorhabditis elegant segmentation using texture-based models for motility phenotyping

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