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

• Published in: PLoS computational biology Vol. 11; no. 4; p. e1004194
• Main Authors: Zhan, Mei, Crane, Matthew M., Entchev, Eugeni V., Caballero, Antonio, Fernandes de Abreu, Diana Andrea, Ch’ng, QueeLim, Lu, Hang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Automation; Caenorhabditis elegans; Caenorhabditis elegans - cytology; Cell Tracking - methods; Classification; Data collection; Data mining; Data processing; Head - anatomy & histology; Image Interpretation, Computer-Assisted - methods; Machine Learning; Methods; Microscopy, Fluorescence - methods; Multimodal Imaging - methods; Neurons; Pattern Recognition, Automated - methods; Physiological aspects; Reproducibility of Results; Scholarships & fellowships; Sensitivity and Specificity; Software
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004194

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 Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans 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.