OC_Finder: Osteoclast Segmentation, Counting, and Classification Using Watershed and Deep Learning

Abstract

Osteoclasts are multinucleated cells that exclusively resorb bone matrix proteins and minerals on the bone surface. They differentiate from monocyte/macrophage lineage cells in the presence of osteoclastogenic cytokines such as the receptor activator of nuclear factor-κB ligand (RANKL) and are stained positive for tartrate-resistant acid phosphatase (TRAP). In vitro osteoclast formation assays are commonly used to assess the capacity of osteoclast precursor cells for differentiating into osteoclasts wherein the number of TRAP-positive multinucleated cells is counted as osteoclasts. Osteoclasts are manually identified on cell culture dishes by human eyes, which is a labor-intensive process. Moreover, the manual procedure is not objective and results in lack of reproducibility. To accelerate the process and reduce the workload for counting the number of osteoclasts, we developed OC_Finder, a fully automated system for identifying osteoclasts in microscopic images. OC_Finder consists of cell image segmentation with a watershed algorithm and cell classification using deep learning. OC_Finder detected osteoclasts differentiated from wild-type and Sh3bp2KI/+ precursor cells at a 99.4% accuracy for segmentation and at a 98.1% accuracy for classification. The number of osteoclasts classified by OC_Finder was at the same accuracy level with manual counting by a human expert. OC_Finder also showed consistent performance on additional datasets collected with different microscopes with different settings by different operators. Together, successful development of OC_Finder suggests that deep learning is a useful tool to perform prompt and accurate unbiased classification and detection of specific cell types in microscopic images.

A Machine Learning-Based Image Segmentation Method to Quantify In Vitro Osteoclast Culture Endpoints

“…It is, therefore, likely that more complex models, such as deep learning (DL), will be required to fully automate the simultaneous quantification of both osteoclast number and resorptive activity. DL has already successfully quantified osteoclast and nuclei numbers [ 22 , 24 , 25 ], but not resorption events. Due to greater processing layers, DL could discover complicated feature patterns in large datasets that better delimit the resorption pit-dentine disc boundary for osteoclast activity analysis.…”

Breast cancer cells promote osteoclast differentiation in an MRTF-dependent paracrine manner

“…To assess OCL differentiation, cells were fixed with 37% formaldehyde for 15 minutes, washed with PBS and stained using leukocyte acid phosphatase kit (Sigma-Aldrich, 387A-1KT) following the manufacturer’s instructions. OCLs (marked by TRAP+ multinucleated cells with three or more nuclei) were counted using the “OC_Finder” program which uses an automated cell segmentation approach before applying deep learning to classify the cells as either OCLs or non-OCLs (61). Total number of cells was counted using ImageJ.…”

Automatic Segmentation of Teeth, Crown–Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

“…Thus, by recognizing similar sequences, similar motifs, and similar pixels, the machine can detect, segment, and classify what those pictures are. The more data there are, the better artificial intelligence features will be revealed [ 1 , 11 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Deep neural networks have many hidden layers, with millions of interconnected artificial neurons.…”