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Contemporary Advances in Computer-Assisted Bone Histomorphometry and Identification of Bone Cells in Culture

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Abstract

Static and dynamic bone histomorphometry and identification of bone cells in culture are labor-intensive and highly repetitive tasks. Several computer-assisted methods have been proposed to ease these tasks and to take advantage of the increased computational power available today. The present review aimed to provide an overview of contemporary methods utilizing specialized computer software to perform bone histomorphometry or identification of bone cells in culture. In addition, a brief historical perspective on bone histomorphometry is included. We identified ten publications using five different computer-assisted approaches (1) ImageJ and BoneJ; (2) Histomorph: OsteoidHisto, CalceinHisto, and TrapHisto; (3) Fiji/ImageJ2 and Trainable Weka Segmentation (TWS); (4) Visiopharm and artificial intelligence (AI); and (5) Osteoclast identification using deep learning with Single Shot Detection (SSD) architecture, Darknet and You Only Look Once (YOLO), or watershed algorithm (OC_Finder). The review also highlighted a substantial need for more validation studies that evaluate the accuracy of the new computational methods to the manual and conventional analyses of histological bone specimens and cells in culture using microscopy. However, a substantial evolution has occurred during the last decade to identify and separate bone cells and structures of interest. Most early studies have used simple image segmentation to separate structures of interest, whereas the most recent studies have utilized AI and deep learning. AI has been proposed to substantially decrease the amount of time needed for analyses and enable unbiased assessments. Despite the clear advantages of highly sophisticated computational methods, the limited nature of existing validation studies, particularly those that assess the accuracy of the third-generation methods compared to the second-generation methods, appears to be an important reason that these techniques have failed to gain wide acceptance.

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Fig. 1
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Adapted from Emmanuel et al. under the terms of the Creative Commons Attribution License (CC BY) [25]

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Adapted from Cohen-Karlik et al. under the terms of the Creative Commons Attribution License (CC BY) [26]

Fig. 5

Adapted from Wang et al. under the terms of the Creative Commons Attribution License (CC BY) [27]

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Authors and Affiliations

  1. Department of Biomedicine, Aarhus University, Wilhelm Meyers Allé 3, 8000, Aarhus, Denmark

    Mikkel Bo Brent

  2. Department of Dermatology, Aarhus University Hospital, 8200, Aarhus, Denmark

    Thomas Emmanuel

  3. Department of Clinical Medicine, Aarhus University Hospital, 8200, Aarhus, Denmark

    Thomas Emmanuel

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Conceptualization, MBB and TE; methodology, MBB and TE; investigation, MBB and TE; resources, MBB and TE; data curation, MBB and TE; writing—original draft preparation, MBB and TE; writing—review and editing, MBB and TE; visualization, MBB and TE. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Mikkel Bo Brent.

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M. B. Brent and T. Emmanuel declare no conflict of interest.

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Brent, M.B., Emmanuel, T. Contemporary Advances in Computer-Assisted Bone Histomorphometry and Identification of Bone Cells in Culture. Calcif Tissue Int 112, 1–12 (2023). https://doi.org/10.1007/s00223-022-01035-2

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