2019
DOI: 10.3389/fphy.2019.00091
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Automation of Bone Tissue Histology

Abstract: In abstract methods of automation of histology, bone structure is considered. Possible inputs are snapshots from a microscope or computed tomography slices. An algorithm is proposed that differentiates objects according to their color (or grayscale) and recover morphology topology. An algorithm to separate morphological objects by their dimensions and color parameters was built. Measured parameters were bone surface, bone area, porosity, cortical thickness, canal number, canal area, and etc. Additionally, we m… Show more

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Cited by 10 publications
(12 citation statements)
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References 34 publications
(35 reference statements)
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“…Sections were stained with hematoxylin-eosin. The histology of bone tissue was analyzed [25,26]. All experiments were performed according to bioethical standards and were approved by the local ethical committee of the Kazan Federal University (Protocol meeting No.2 of May 5, 2015).…”
Section: Methodsmentioning
confidence: 99%
“…Sections were stained with hematoxylin-eosin. The histology of bone tissue was analyzed [25,26]. All experiments were performed according to bioethical standards and were approved by the local ethical committee of the Kazan Federal University (Protocol meeting No.2 of May 5, 2015).…”
Section: Methodsmentioning
confidence: 99%
“…We used a specific algorithm to analyze the CT data. Generally, the algorithm of the analysis can be described as [25,29,31,32]: 1) calculating the binarization threshold; 2) meshing the sample;…”
Section: mentioning
confidence: 99%
“…One of them is based on approximating the inhomogeneity by constructing a distribution using the mean intercept length method. With this approach, the physical relations are expressed in terms of the tensor of elastic constants and the fabric tensor (Sachenkov et al, 2016;Kayumov et al, 2018;Yaikova et al, 2019). The next approach is based on reducing the material anisotropy to orthotropy by calculating constants from numerical experiments (Ruess et al, 2012;Ridwan-Pramana et al, 2017;Chikova et al, 2018).…”
Section: Introductionmentioning
confidence: 99%