A novel measure and significance testing in data analysis of cell image segmentation
Identifieur interne : 000F12 ( Main/Curation ); précédent : 000F11; suivant : 000F13A novel measure and significance testing in data analysis of cell image segmentation
Auteurs : Jin Chu Wu ; Michael Halter ; Raghu N. Kacker ; John T. Elliott ; Anne L. PlantSource :
- BMC Bioinformatics [ 1471-2105 ] ; 2017.
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Abstract
Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based cell assays in cytometry. Many measures and methods have been proposed and implemented to evaluate segmentation methods. However, computing the standard errors (SE) of the measures and their correlation coefficient is not described, and thus the statistical significance of performance differences between CIS algorithms cannot be assessed.
We propose the total error rate (TER), a novel performance measure for segmenting all cells in the supervised evaluation. The TER statistically aggregates all misclassification error rates (MER) by taking cell sizes as weights. The MERs are for segmenting each single cell in the population. The TER is fully supported by the pairwise comparisons of MERs using 106 manually segmented ground-truth cells with different sizes and seven CIS algorithms taken from ImageJ. Further, the SE and 95% confidence interval (CI) of TER are computed based on the SE of MER that is calculated using the bootstrap method. An algorithm for computing the correlation coefficient of TERs between two CIS algorithms is also provided. Hence, the 95% CI error bars can be used to classify CIS algorithms. The SEs of TERs and their correlation coefficient can be employed to conduct the hypothesis testing, while the CIs overlap, to determine the statistical significance of the performance differences between CIS algorithms.
A novel measure TER of CIS is proposed. The TER’s SEs and correlation coefficient are computed. Thereafter, CIS algorithms can be evaluated and compared statistically by conducting the significance testing.
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DOI: 10.1186/s12859-017-1527-x
PubMed: 28292256
PubMed Central: 5351215
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Elliott</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Plant, Anne L" sort="Plant, Anne L" uniqKey="Plant A" first="Anne L." last="Plant">Anne L. 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Kacker</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Elliott, John T" sort="Elliott, John T" uniqKey="Elliott J" first="John T." last="Elliott">John T. Elliott</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Plant, Anne L" sort="Plant, Anne L" uniqKey="Plant A" first="Anne L." last="Plant">Anne L. Plant</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">BMC Bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Image Interpretation, Computer-Assisted</term><term>Mice</term><term>Microscopy, Fluorescence</term><term>Myocytes, Smooth Muscle (cytology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Animaux</term><term>Interprétation d'image assistée par ordinateur</term><term>Microscopie de fluorescence</term><term>Myocytes du muscle lisse (cytologie)</term><term>Souris</term></keywords><keywords scheme="MESH" qualifier="cytologie" xml:lang="fr"><term>Myocytes du muscle lisse</term></keywords><keywords scheme="MESH" qualifier="cytology" xml:lang="en"><term>Myocytes, Smooth Muscle</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Image Interpretation, Computer-Assisted</term><term>Mice</term><term>Microscopy, Fluorescence</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Animaux</term><term>Interprétation d'image assistée par ordinateur</term><term>Microscopie de fluorescence</term><term>Souris</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based cell assays in cytometry. Many measures and methods have been proposed and implemented to evaluate segmentation methods. However, computing the standard errors (SE) of the measures and their correlation coefficient is not described, and thus the statistical significance of performance differences between CIS algorithms cannot be assessed.</p></sec><sec><title>Results</title><p>We propose the total error rate (TER), a novel performance measure for segmenting all cells in the supervised evaluation. The TER statistically aggregates all misclassification error rates (MER) by taking cell sizes as weights. The MERs are for segmenting each single cell in the population. The TER is fully supported by the pairwise comparisons of MERs using 106 manually segmented ground-truth cells with different sizes and seven CIS algorithms taken from ImageJ. Further, the SE and 95% confidence interval (CI) of TER are computed based on the SE of MER that is calculated using the bootstrap method. An algorithm for computing the correlation coefficient of TERs between two CIS algorithms is also provided. Hence, the 95% CI error bars can be used to classify CIS algorithms. The SEs of TERs and their correlation coefficient can be employed to conduct the hypothesis testing, while the CIs overlap, to determine the statistical significance of the performance differences between CIS algorithms.</p></sec><sec><title>Conclusions</title><p>A novel measure TER of CIS is proposed. The TER’s SEs and correlation coefficient are computed. 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