Orthopaedic imaging for translational research and clinical application

Cheng, Xiaoguang; Wáng, Yì Xiáng J.

doi:10.1016/j.jot.2018.10.007

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Skeletal measurement is the pillar of many research applications, such as developmental studies on limb patterning ( Summerbell, 1977 ; Galloway et al, 2009 ) and growth ( Rosello-Diez et al, 2017 ; Marchini and Rolian, 2018 ), main axis segmentation ( Casaca et al, 2014 ; Wong et al, 2015 ), evolutionary studies ( Sears et al, 2006 ; Sheth et al, 2012 ; Kherdjemil et al, 2016 ), disease modeling ( Chen et al, 1999 ; Li et al, 1999 ; Rowe et al, 2018 ), adult phenotyping of mutant models ( Boskey et al, 2003 ), etc. Whereas clinical musculoskeletal research often uses non-destructive imaging as routine ( Cheng and Wang, 2018 ), fundamental evolutionary and development (evo-devo) studies often rely on differential staining of bone and cartilage (the so-called ex vivo skeletal preparations) ( Rigueur and Lyons, 2014 ; Mead, 2020 ) and subsequent two-dimensional (2D) imaging for quantitative comparisons of the models of interest. Despite being broadly used, the skeletal preparation technique is ridden by several disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography

Diaz

H’ng

et al. 2021

Front. Cell Dev. Biol.

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The characterization of developmental phenotypes often relies on the accurate linear measurement of structures that are small and require laborious preparation. This is tedious and prone to errors, especially when repeated for the multiple replicates that are required for statistical analysis, or when multiple distinct structures have to be analyzed. To address this issue, we have developed a pipeline for characterization of long-bone length using X-ray microtomography (XMT) scans. The pipeline involves semi-automated algorithms for automatic thresholding and fast interactive isolation and 3D-model generation of the main limb bones, using either the open-source ImageJ plugin BoneJ or the commercial Mimics Innovation Suite package. The tests showed the appropriate combination of scanning conditions and analysis parameters yields fast and comparable length results, highly correlated with the measurements obtained via ex vivo skeletal preparations. Moreover, since XMT is not destructive, the samples can be used afterward for histology or other applications. Our new pipelines will help developmental biologists and evolutionary researchers to achieve fast, reproducible and non-destructive length measurement of bone samples from multiple animal species.

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Section: Introductionmentioning

confidence: 99%

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography

Diaz

H’ng

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

show abstract

“…Skeletal measurement is the pillar of many research applications, such as developmental studies on limb patterning (Galloway et al, 2009;Summerbell, 1977) and growth (Marchini and Rolian, 2018;Rosello-Diez et al, 2017), main axis segmentation (Casaca et al, 2014;Wong et al, 2015), evolutionary studies (Kherdjemil et al, 2016;Sears et al, 2006;Sheth et al, 2012), disease modelling (Chen et al, 1999;Rowe et al, 2018), adult phenotyping of mutant models (Boskey et al, 2003), etc. Whereas clinical musculoskeletal research often uses non-destructive imaging as routine (Cheng and Wang, 2018), fundamental evolutionary and development (evo-devo) studies often rely on differential staining of bone and cartilage (the so-called ex vivo skeletal preparations) (Mead, 2020;Rigueur and Lyons, 2014) and subsequent two-dimensional (2D) imaging for quantitative comparisons of the models of interest. Despite being broadly used, the skeletal preparation technique is ridden by several disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A new pipeline to automatically segment and semi-automatically measure bone length on 3D models obtained by Computed Tomography

Diaz

Fung

et al. 2020

Preprint

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statement: Beltran Diaz et al. present a semi-automated pipeline for fast and versatile characterization of bone length from micro-CT images of mouse developmental samples. Abstract:The characterization of developmental phenotypes often relies on the accurate linear measurement of structures that are small and require laborious preparation. This is tedious and prone to errors, especially when repeated for the multiple replicates that are required for statistical analysis, or when multiple distinct structures have to be analysed. To address this issue, we have developed a pipeline for characterization of long-bone length using micro-CT scans. It involves a semi-automated algorithm that uses the Mimics Innovation Suite package (Materialise) for automatic thresholding and fast interactive isolation and 3D-model generation of the main limb bones. All the image-processing steps are included in a user-friendly Python script. We show that the appropriate combination of scanning and in silico analysis conditions yields fast and reproducible length results, highly correlated with the measurements obtained via ex vivo skeletal preparations. Moreover, since micro-CT is not destructive, the samples can be used afterwards for histology or other applications. Our new pipeline will help developmental biologists and evolution researchers to achieve fast, reproducible and non-destructive length measurement of bone samples from multiple animal species.

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Orthopaedic imaging for translational research and clinical application

Cited by 2 publications

References 10 publications

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography

A new pipeline to automatically segment and semi-automatically measure bone length on 3D models obtained by Computed Tomography

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