TY - JOUR
T1 - Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning
AU - Mirsky, Simcha K.
AU - Barnea, Itay
AU - Levi, Mattan
AU - Greenspan, Hayit
AU - Shaked, Natan T.
N1 - Publisher Copyright:
© 2017 International Society for Advancement of Cytometry
PY - 2017/9
Y1 - 2017/9
N2 - Currently, the delicate process of selecting sperm cells to be used for in vitro fertilization (IVF) is still based on the subjective, qualitative analysis of experienced clinicians using non-quantitative optical microscopy techniques. In this work, a method was developed for the automated analysis of sperm cells based on the quantitative phase maps acquired through use of interferometric phase microscopy (IPM). Over 1,400 human sperm cells from 8 donors were imaged using IPM, and an algorithm was designed to digitally isolate sperm cell heads from the quantitative phase maps while taking into consideration both the cell 3D morphology and contents, as well as acquire features describing sperm head morphology. A subset of these features was used to train a support vector machine (SVM) classifier to automatically classify sperm of good and bad morphology. The SVM achieves an area under the receiver operating characteristic curve of 88.59% and an area under the precision-recall curve of 88.67%, as well as precisions of 90% or higher. We believe that our automatic analysis can become the basis for objective and automatic sperm cell selection in IVF.
AB - Currently, the delicate process of selecting sperm cells to be used for in vitro fertilization (IVF) is still based on the subjective, qualitative analysis of experienced clinicians using non-quantitative optical microscopy techniques. In this work, a method was developed for the automated analysis of sperm cells based on the quantitative phase maps acquired through use of interferometric phase microscopy (IPM). Over 1,400 human sperm cells from 8 donors were imaged using IPM, and an algorithm was designed to digitally isolate sperm cell heads from the quantitative phase maps while taking into consideration both the cell 3D morphology and contents, as well as acquire features describing sperm head morphology. A subset of these features was used to train a support vector machine (SVM) classifier to automatically classify sperm of good and bad morphology. The SVM achieves an area under the receiver operating characteristic curve of 88.59% and an area under the precision-recall curve of 88.67%, as well as precisions of 90% or higher. We believe that our automatic analysis can become the basis for objective and automatic sperm cell selection in IVF.
KW - holography
KW - in vitro fertilization
KW - interference microscopy
KW - machine learning
KW - spermatozoa
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85029661023&partnerID=8YFLogxK
U2 - 10.1002/cyto.a.23189
DO - 10.1002/cyto.a.23189
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AN - SCOPUS:85029661023
SN - 1552-4922
VL - 91
SP - 893
EP - 900
JO - Cytometry. Part A : the journal of the International Society for Analytical Cytology
JF - Cytometry. Part A : the journal of the International Society for Analytical Cytology
IS - 9
ER -