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Authordc.contributor.authorChang, Violeta 
Authordc.contributor.authorGarcía, Alejandra 
Authordc.contributor.authorHitschfeld Kahler, Nancy 
Authordc.contributor.authorHärtel, Steffen 
Admission datedc.date.accessioned2019-05-29T13:10:44Z
Available datedc.date.available2019-05-29T13:10:44Z
Publication datedc.date.issued2017
Cita de ítemdc.identifier.citationComputers in Biology and Medicine 83 (2017) 143–150
Identifierdc.identifier.issn18790534
Identifierdc.identifier.issn00104825
Identifierdc.identifier.other10.1016/j.compbiomed.2017.03.004
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/168864
Abstractdc.description.abstractBackground and Objective: Published algorithms for classification of human sperm heads are based on relatively small image databases that are not open to the public, and thus no direct comparison is available for competing methods. We describe a gold-standard for morphological sperm analysis (SCIAN-MorphoSpermGS), a dataset of sperm head images with expert-classification labels in one of the following classes: normal, tapered, pyriform, small or amorphous. This gold-standard is for evaluating and comparing known techniques and future improvements to present approaches for classification of human sperm heads for semen analysis. Although this paper does not provide a computational tool for morphological sperm analysis, we present a set of experiments for comparing sperm head description and classification common techniques. This classification base-line is aimed to be used as a reference for future improvements to present approaches for human sperm head classification. Methods: The gold-standard provides a label for each sperm head, which is achieved by majority voting among experts. The classification base-line compares four supervised learning methods (1 - Nearest Neighbor, naive Bayes, decision trees and Support Vector Machine (SVM)) and three shape-based descriptors (Hu moments, Zernike moments and Fourier descriptors), reporting the accuracy and the true positive rate for each experiment. We used Fleiss' Kappa Coefficient to evaluate the inter-expert agreement and Fisher's exact test for inter-expert variability and statistical significant differences between descriptors and learning techniques. Results: Our results confirm the high degree of inter-expert variability in the morphological sperm analysis. Regarding the classification base line, we show that none of the standard descriptors or classification approaches is best suitable for tackling the problem of sperm head classification. We discovered that the correct classification rate was highly variable when trying to discriminate among non-normal sperm heads. By using the Fourier descriptor and SVM, we achieved the best mean correct classification: only 49%. Conclusions: We conclude that the SCIAN-MorphoSpermGS will provide a standard tool for evaluation of characterization and classification approaches for human sperm heads. Indeed, there is a clear need for a specific shape-based descriptor for human sperm heads and a specific classification approach to tackle the problem of high variability within subcategories of abnormal sperm cells.
Lenguagedc.language.isoen
Publisherdc.publisherElsevier
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
Sourcedc.sourceComputers in Biology and Medicine
Keywordsdc.subjectGold-standard
Keywordsdc.subjectInfertility
Keywordsdc.subjectMorphological sperm analysis
Keywordsdc.subjectSperm classification base-line
Keywordsdc.subjectSperm head classification
Títulodc.titleGold-standard for computer-assisted morphological sperm analysis
Document typedc.typeArtículo de revista
Catalogueruchile.catalogadorlaj
Indexationuchile.indexArtículo de publicación SCOPUS
uchile.cosechauchile.cosechaSI


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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile