Biomedical subjects

Pamela Cosman

Publications and source records attributed to Pamela Cosman.

4 recordsLinked to original sources

PubMed2006

Machine vision based detection of omega bends and reversals in C. elegans.

The behavior of the nematode Caenorhabditis elegans has proven increasingly useful for the genetic dissection of neurobiological signaling pathways and for investigating the neural and molecular basis of nervous system function. Locomotion is among the most complex aspects of C. elegans behavior, and involves a number of discrete motor activities such as omega bends (deep bends typically on the ventral side of the body which reorient the direction of forward locomotion) and reversals (changes in the direction of the locomotion wave that cause a switch from forward to backward crawling). Reliable methods for detecting and quantifying these movements are critical for escape reflexes and navigation behaviors. Here we describe a novel algorithm to automatically detect omega bends, which relies in part on a new method for obtaining a morphological skeleton describing the body posture of coiled worms. We also present an optimized algorithm to detect reversals, which showed improved performance over previously described methods. Together, these new algorithms have made it possible to reliably detect events that are time-consuming and laborious to detect by real-time observation or human video analysis. They have also made it possible to identify mutants with subtle behavioral abnormalities, such as those in which omega bends are dorsoventrally unbiased or uncorrelated with reversals. These methods should therefore facilitate quantitative analysis of a wide range of locomotion-related behaviors in this important neurobiological model organism.

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PubMed2004

Automatic tracking, feature extraction and classification of C elegans phenotypes.

This paper presents a method for automatic tracking of the head, tail, and entire body movement of the nematode Caenorhabditis elegans (C. elegans) using computer vision and digital image analysis techniques. The characteristics of the worm's movement, posture and texture information were extracted from a 5-min image sequence. A Random Forests classifier was then used to identify the worm type, and the features that best describe the data. A total of 1597 individual worm video sequences, representing wild type and 15 different mutant types, were analyzed. The average correct classification ratio, measured by out-of-bag (OOB) error rate, was 90.9%. The features that have most discrimination ability were also studied. The algorithm developed will be an essential part of a completely automated C. elegans tracking and identification system.

Algorithms↗

PubMed2003

Quantitative classification and natural clustering of Caenorhabditis elegans behavioral phenotypes.

Genetic analysis of nervous system function relies on the rigorous description of behavioral phenotypes. However, standard methods for classifying the behavioral patterns of mutant Caenorhabditis elegans rely on human observation and are therefore subjective and imprecise. Here we describe the application of machine learning to quantitatively define and classify the behavioral patterns of C. elegans nervous system mutants. We have used an automated tracking and image processing system to obtain measurements of a wide range of morphological and behavioral features from recordings of representative mutant types. Using principal component analysis, we represented the behavioral patterns of eight mutant types as data clouds distributed in multidimensional feature space. Cluster analysis using the k-means algorithm made it possible to quantitatively assess the relative similarities between different behavioral phenotypes and to identify natural phenotypic clusters among the data. Since the patterns of phenotypic similarity identified in this study closely paralleled the functional similarities of the mutant gene products, the complex phenotypic signatures obtained from these image data appeared to represent an effective diagnostic of the mutants' underlying molecular defects.

Animals↗

PubMed2002

Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively.

Mutants with abnormal patterns of locomotion, also known as uncoordinated (Unc) mutants, have facilitated the genetic dissection of many important aspects of nervous system function and development in the nematode Caenorhabditis elegans. Although a large number of distinct classes of Unc mutants can be distinguished by an experienced observer, precise quantitative definitions of these classes have not been available. Here we describe a new approach for using automatically-acquired image data to quantify the locomotion patterns of wild-type and mutant worms. We designed an automated tracking and imaging system capable of following an individual animal for long time periods and saving a time-coded series of digital images representing its motion and body posture over the course of the recording. We have also devised methods for measuring specific features from these image data that can be used by the classification and regression tree classification algorithm to reliably identify the behavioral patterns of specific mutant types. Ultimately, these tools should make it possible to evaluate with quantitative precision the behavioral phenotypes of novel mutants, gene knockout lines, or pharmacological treatments.

Algorithms↗