Violeta Chang1, Alejandra Garcia2, Nancy Hitschfeld3, Steffen Härtel4. 1. Department of Computer Science, University of Chile, Beauchef 851, 3rd Floor, Santiago, RM, Chile; Laboratory for Scientific Image Analysis, SCIAN-Lab, Centro de Espermiograma Digital Asistido por Internet (CEDAI SpA), Centro de Informatica Medica y Telemedicina (CIMT), Centro Nacional en Sistemas de Informacion en Salud (CENS), Biomedical Neuroscience Institute (BNI), Instituo de Ciencias Biomedicas (ICBM), Faculty of Medicine, University of Chile, Av. Independencia 1027, Independencia, RM, Chile. Electronic address: vchang@dcc.uchile.cl. 2. Laboratory for Scientific Image Analysis, SCIAN-Lab, Centro de Espermiograma Digital Asistido por Internet (CEDAI SpA), Centro de Informatica Medica y Telemedicina (CIMT), Centro Nacional en Sistemas de Informacion en Salud (CENS), Biomedical Neuroscience Institute (BNI), Instituo de Ciencias Biomedicas (ICBM), Faculty of Medicine, University of Chile, Av. Independencia 1027, Independencia, RM, Chile. Electronic address: al_garcia@med.uchile.cl. 3. Department of Computer Science, University of Chile, Beauchef 851, 3rd Floor, Santiago, RM, Chile. Electronic address: nancy@dcc.uchile.cl. 4. Laboratory for Scientific Image Analysis, SCIAN-Lab, Centro de Espermiograma Digital Asistido por Internet (CEDAI SpA), Centro de Informatica Medica y Telemedicina (CIMT), Centro Nacional en Sistemas de Informacion en Salud (CENS), Biomedical Neuroscience Institute (BNI), Instituo de Ciencias Biomedicas (ICBM), Faculty of Medicine, University of Chile, Av. Independencia 1027, Independencia, RM, Chile. Electronic address: shartel@med.uchile.cl.
Abstract
BACKGROUND 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.
BACKGROUND 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.
Authors: Xiaoqing Pan; Kang Gao; Ning Yang; Yafei Wang; Xiaodong Zhang; Le Shao; Pin Zhai; Feng Qin; Xia Zhang; Jian Li; Xinglong Wang; Jie Yang Journal: Front Vet Sci Date: 2022-06-30
Authors: Christopher McCallum; Jason Riordon; Yihe Wang; Tian Kong; Jae Bem You; Scott Sanner; Alexander Lagunov; Thomas G Hannam; Keith Jarvi; David Sinton Journal: Commun Biol Date: 2019-07-03