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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.
, 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 October 8, 2025)