Overview of biological structure detection using multi-tiered classification.

<p>a) Unsupervised image processing techniques are often necessary to harness the power of emerging imaging and experimental technologies. b) An overview of the proposed generalizable two layer classification architecture for the autonomous identification of specific biological structures. Intrinsic, computationally simple features and relational or computationally expensive features are partitioned into two layers to accommodate both structural complexity and efficiency.</p

Second layer classifier for cell pattern recognition and identification.

<p>a) Representative maximum intensity projection and schematic representation of the two neuron pairs in which an insulin-like peptide is expressed. b) The modularity of our scheme permits the preprocessing and layer 1 classification components from neuron pair detection to be re-used for the recognition and identification of these neuron pairs. c) To identify the pattern with the appropriate cell identifications, properties for all possible combinations and arrangements of the layer 1 candidates are calculated. Here, all six such candidate sets for 4 candidate particles are shown. d) Validation of the SVM classifier trained with these features shows high specificity but only moderate sensitivity. e) The lower sensitivity observed for this classification scheme is mainly due to the limit ability to accommodate biological deviations from the stereotypical arrangement of the neurons while still maintaining high specificity.</p

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using <i>Caenorhabditis elegans</i>

<div><p>Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism <i>Caenorhabditis elegans</i>. To serve a common need for automated high-resolution imaging and behavior applications in the <i>C</i>. <i>elegans</i> research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods.</p></div

Head versus tail classification using grinder detection is robust to changes in experimental conditions and genetic background.

<p>a) Changes in experimental conditions, such as food availability, can alter the bulk morphology and the appearance of worm body in bright field, with potential consequences for classification accuracy. b) Our head versus tail classification scheme maintains sensitivity and specificity at over 95% at different ages and feeding conditions despite these biological changes. c) Genetic changes can also induce changes in bulk morphology and texture of the worm. d) Despite not being represented within the training set, the performance of the classifier is maintained even for mutant worms (<i>dpy-4 (-)</i>) with major morphological changes. e) Changes in the optics, camera or acquisition parameters can alter the final resolution of images. f) The inclusion of the calibration metric within feature calculation (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004194#pcbi.1004194.s002" target="_blank">S2 Fig</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004194#pcbi.1004194.s003" target="_blank">S3 Fig</a>) maintains classifier performance across a two-fold change in image resolution due to alternations in digital binning.</p

Optimization and training of the two layers of SVM classification for pharyngeal grinder detection.

<p>a) To construct the layer 1 classifier with the specified feature set, five-fold cross-validation with a manually annotated training set is first used to optimize SVM model parameters and ensure classification performance. b) Classification performance based on the false positive (FPR) and false negative (FNR) error rates observed in five-fold cross-validation allows selection of an optimal parameter set. c) The full training set and optimized parameters are used to construct the final layer 1 SVM model. Linear projections of the training set features onto two dimensions show that the layer 1 feature set and the optimized SVM model are insufficient for identifying the grinder particle with high specificity. d) The second layer of classification refines the final classification decision and is parameter-optimized using the candidates passed from layer 1 of classification. e) Classification performance based on five-fold cross-validation is used for parameter selection. f) The reduced layer 2 training set and optimized parameters are used to construct the final layer 2 SVM model. Linear projections of layer 2 features for the training set demonstrate the capability of a two layer scheme for the detection of the grinder with both high specificity and sensitivity.</p

Second layer classification for neuron pair detection.

<p>a) The first layer of classification is insufficient for rejection of all background particles. b) The reduced candidate set from the first layer of classification is used to form candidate cell pairs with feature sets describing their relative positioning and intensities. c) Although classification based on these features is sufficient for accurate cell pair detection in the majority of cases (left), multiple potential cell pairs are sometimes classified within the same image (right). d) Incorporating probability estimates (shown in panel c) into the SVM model and selecting the most likely cell pair eliminates these false positives and increases the specificity of the classifier.</p