TY - JOUR
T1 - Quantification of Osteoclasts in Culture, Powered by Machine Learning
AU - Cohen-Karlik, Edo
AU - Awida, Zamzam
AU - Bergman, Ayelet
AU - Eshed, Shahar
AU - Nestor, Omer
AU - Kadashev, Michelle
AU - Yosef, Sapir Ben
AU - Saed, Hussam
AU - Mansour, Yishay
AU - Globerson, Amir
AU - Neumann, Drorit
AU - Gabet, Yankel
N1 - Publisher Copyright:
© Copyright © 2021 Cohen-Karlik, Awida, Bergman, Eshed, Nestor, Kadashev, Yosef, Saed, Mansour, Globerson, Neumann and Gabet.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - In vitro osteoclastogenesis is a central assay in bone biology to study the effect of genetic and pharmacologic cues on the differentiation of bone resorbing osteoclasts. To date, identification of TRAP+ multinucleated cells and measurements of osteoclast number and surface rely on a manual tracing requiring specially trained lab personnel. This task is tedious, time-consuming, and prone to operator bias. Here, we propose to replace this laborious manual task with a completely automatic process using algorithms developed for computer vision. To this end, we manually annotated full cultures by contouring each cell, and trained a machine learning algorithm to detect and classify cells into preosteoclast (TRAP+ cells with 1–2 nuclei), osteoclast type I (cells with more than 3 nuclei and less than 15 nuclei), and osteoclast type II (cells with more than 15 nuclei). The training usually requires thousands of annotated samples and we developed an approach to minimize this requirement. Our novel strategy was to train the algorithm by working at “patch-level” instead of on the full culture, thus amplifying by >20-fold the number of patches to train on. To assess the accuracy of our algorithm, we asked whether our model measures osteoclast number and area at least as well as any two trained human annotators. The results indicated that for osteoclast type I cells, our new model achieves a Pearson correlation (r) of 0.916 to 0.951 with human annotators in the estimation of osteoclast number, and 0.773 to 0.879 for estimating the osteoclast area. Because the correlation between 3 different trained annotators ranged between 0.948 and 0.958 for the cell count and between 0.915 and 0.936 for the area, we can conclude that our trained model is in good agreement with trained lab personnel, with a correlation that is similar to inter-annotator correlation. Automation of osteoclast culture quantification is a useful labor-saving and unbiased technique, and we suggest that a similar machine-learning approach may prove beneficial for other morphometrical analyses.
AB - In vitro osteoclastogenesis is a central assay in bone biology to study the effect of genetic and pharmacologic cues on the differentiation of bone resorbing osteoclasts. To date, identification of TRAP+ multinucleated cells and measurements of osteoclast number and surface rely on a manual tracing requiring specially trained lab personnel. This task is tedious, time-consuming, and prone to operator bias. Here, we propose to replace this laborious manual task with a completely automatic process using algorithms developed for computer vision. To this end, we manually annotated full cultures by contouring each cell, and trained a machine learning algorithm to detect and classify cells into preosteoclast (TRAP+ cells with 1–2 nuclei), osteoclast type I (cells with more than 3 nuclei and less than 15 nuclei), and osteoclast type II (cells with more than 15 nuclei). The training usually requires thousands of annotated samples and we developed an approach to minimize this requirement. Our novel strategy was to train the algorithm by working at “patch-level” instead of on the full culture, thus amplifying by >20-fold the number of patches to train on. To assess the accuracy of our algorithm, we asked whether our model measures osteoclast number and area at least as well as any two trained human annotators. The results indicated that for osteoclast type I cells, our new model achieves a Pearson correlation (r) of 0.916 to 0.951 with human annotators in the estimation of osteoclast number, and 0.773 to 0.879 for estimating the osteoclast area. Because the correlation between 3 different trained annotators ranged between 0.948 and 0.958 for the cell count and between 0.915 and 0.936 for the area, we can conclude that our trained model is in good agreement with trained lab personnel, with a correlation that is similar to inter-annotator correlation. Automation of osteoclast culture quantification is a useful labor-saving and unbiased technique, and we suggest that a similar machine-learning approach may prove beneficial for other morphometrical analyses.
KW - artificial intelligence
KW - automatic quantification of osteoclasts
KW - convolutional neural network (CNN)
KW - deep learning
KW - deep neural networks (DNN)
KW - machine learning
KW - object detection
KW - osteoclasts
UR - http://www.scopus.com/inward/record.url?scp=85107421035&partnerID=8YFLogxK
U2 - 10.3389/fcell.2021.674710
DO - 10.3389/fcell.2021.674710
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C2 - 34113621
AN - SCOPUS:85107421035
SN - 2296-634X
VL - 9
JO - Frontiers in Cell and Developmental Biology
JF - Frontiers in Cell and Developmental Biology
M1 - 674710
ER -