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Measurement Uncertainty in Cell Image Segmentation Data Analysis

Published

Author(s)

Jin Chu Wu, Michael W. Halter, Raghu N. Kacker, John T. Elliott, Anne L. Plant

Abstract

Cell image segmentation is a part of quantitative studies regarding cell movement and cell behavior, and it plays a critical role in molecular biology and cellular biochemistry. Therefore, it is fundamentally important to evaluate the performance levels of cell image segmentation algorithms. In our previous study, the performance metrics for cell image segmentation algorithms were proposed. The sampling variability can result in measurement uncertainties. In this article, the uncertainty of the measure, i.e., the total error rate, in the cell image segmentation is computed in terms of standard error and 95 % confidence interval using bootstrap method as well as an analytical method. Examples are provided.
Citation
NIST Interagency/Internal Report (NISTIR) - 7954
Report Number
7954

Keywords

Cell image segmentation, Misclassification error rate, Total error rate, Uncertainty, Standard error, Confidence interval, Bootstrap, Analytical method.

Citation

, J. , Halter, M. , Kacker, R. , Elliott, J. and Plant, A. (2013), Measurement Uncertainty in Cell Image Segmentation Data Analysis, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.7954 (Accessed February 22, 2024)
Created August 13, 2013, Updated November 10, 2018