Segmentation of nanotomographic cortical bone images for quantitative characterization of the osteoctyte lacuno-canalicular network

Ciani, Antonia; Guizar‐Sicairos, Manuel; Díaz, Ana; Holler, Mirko; Pallu, Stéphane; Achiou, Z.; Jennane, Rachid; Toumi, Hechmi; Lespessailles, Éric; Kewish, Cameron M.

doi:10.1063/1.4937534

Cited by 4 publications

(12 citation statements)

References 21 publications

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“…The total X-ray dose imparted to the specimens can be estimated from the incident flux density ( Howells et al, 2009 ), which was different when using the pinhole (experiment 1) and the Fresnel zone plate (FZP) (experiments 2 and 3) to define the beam on the sample. Assuming a bone composition of pure hydroxyapatite [Ca 10 (PO 4 ) 6 (OH) 2 ] with density 2 g/cm 3 , and X-ray flux densities of 2.3 × 10 5 photons/μm 2 for the pinhole and 1.3 × 10 7 photons/μm 2 when using the FZP, we estimate that doses of the order of 10 6 Gy and 5.6 × 10 7 Gy, respectively, were imparted to the bone specimens during PXCT scanning ( Ciani et al, 2016 ). At these X-ray doses, it would only be feasible to image organic tissue intact with cryogenic protection against radiation damage ( Howells et al, 2009 ).…”

Section: Methodsmentioning

confidence: 98%

“…Cortical bone samples from the tibial diaphysis of rat bones were analyzed through a quantitative method we have developed, previously reported by Ciani et al (2016) which provides a microarchitectural characterization of the LCN in terms of a morphological description of the osteocyte lacunae and a geometrical description of the canaliculi. Fig.…”

Section: Introductionmentioning

confidence: 99%

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Ptychographic X-ray CT characterization of the osteocyte lacuno-canalicular network in a male rat's glucocorticoid induced osteoporosis model

et al. 2018

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Ptychographic X-ray computed tomography (PXCT) is a quantitative imaging modality that non-destructively maps the 3D electron density inside an object with tens of nanometers spatial resolution. This method provides unique access to the morphology and structure of the osteocyte lacuno-canalicular network (LCN) and nanoscale density of the tissue in the vicinity of an osteocyte lacuna. Herein, we applied PXCT to characterize the lacunae and LCN in a male Wistar rat model of glucocorticoid-induced osteoporosis (GIO). The ptychographic images revealed significant (p < 0.05) differences in the number of canaliculi originating from the lacuna per ellipsoidal surface unit, Ca.Nb (p = 0.0106), and the 3D morphology of the lacuna (p = 0.0064), between GIO and SHAM groups. Moreover, the mean canalicular diameter, Ca.Dm, was slightly statistically un-significantly smaller in GIO (152 ± 6.5) nm than in SHAM group (165 ± 8) nm (p = 0.053). Our findings indicate that PXCT can non-destructively provide detailed, nanoscale information on the 3D organization of the LCN in correlative studies of pathologies, such as osteoporosis, leading to improved diagnosis and therapy.

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Section: Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Ptychographic X-ray CT characterization of the osteocyte lacuno-canalicular network in a male rat's glucocorticoid induced osteoporosis model

et al. 2018

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“…Very few publications have recently characterised osteocyte lacunae and LCN ptychographic parameters in rats. [91][92][93] Main parameters used are presented in Table 1.…”

Section: Ptychographic X-ray Ctmentioning

confidence: 99%

“…Ciani et al 92,93 applied SR ptychographic X-ray CT to characterise the lacunae and LCN in a male Wistar rat model of glucocorticoid-induced osteoporosis (GIO). After acclimation, at 19 weeks of age, the rats were randomly assigned to a control group (SHAM) or a methylprednisolone-treated group (GIO).…”

Section: Ptychographic X-ray Ctmentioning

confidence: 99%

“…LcV1: number of empty voxels excluding canaliculi, LcV2: volume lacuna obtained by a fitting procedure. The optimised fit residual, Lc.Fopt is the root-meansquare (rms) difference between the best-fit ellipsoid and the lacuna surface, 92 which gives a measure of the surface irregularity of the lacuna, Ca.Dm = canaliculi diameter, Ca.V/MV = microporosity as the ratio of the volume occupied by canaliculi to the total matrix tissue volume; Ca.Nb = number of canaliculi originating from the lacuna per ellipsoidal surface unit; Ca.Pr = mean number of primary canaliculi directly connected to the lacuna; Ca.Db = length of the first dendritic branching distance.…”

Section: Ptychographic X-ray Ctmentioning

confidence: 99%

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New insights in osteocyte imaging by synchrotron radiation

Portier

Jaffré

Kewish

et al. 2020

J. Spectral Imaging

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Bone health depends on the bone mineral density and mechanical strength, characterised quantitatively and inferred through qualitative parameters such as the trabecular and cortical micro-architecture, and other parameters describing the bone cells. Among these cells, the osteocyte has been recognised as the orchestrator of bone remodelling, playing a key role in directing osteoblastic and osteoclastic activities. Conventional optical and electron microscopies have greatly improved our understanding of the cell physiology mechanisms involved in different osteoarticular pathophysiological contexts, especially osteoporosis. More recently, imaging methods exploiting synchrotron radiation, such as X-ray tomography, ptychography, and deep ultraviolet and Fourier transform infrared spectroscopies, have revealed new biochemical, chemical and 3D morphological information about the osteocyte lacuna, the surrounding matrix and the lacuna–canalicular network at spatial length scales spanning microns to tens of nanometres. Here, we review recent results in osteocyte lacuna and lacuna–canalicular network characterisation by synchrotron radiation imaging in human and animal models, contributing to new insights in different physiologic and pathophysiological situations.

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Deep learning models to map osteocyte networks can successfully distinguish between young and aged bone

Vetter,

Schurman,

Alliston

et al. 2023

Preprint

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Osteocytes, the most abundant and mechanosensitive cells in bone tissue, play a pivotal role in bone homeostasis and mechano-responsiveness, orchestrating the intricate balance between bone formation and resorption under daily activity. Studying osteocyte connectivity and understanding their intricate arrangement within the lacunar canalicular network (LCN) is essential for unraveling bone physiology. This is particularly true as our bones age, which is associated with decreased integrity of the osteocyte network, disrupted mass transport, and lower sensitivity to the mechanical stimuli that allow the skeleton to adapt to changing demands. Much work has been carried out to investigate this relationship, often involving high resolution microscopy of discrete fragments of this network, alongside advanced computational modelling of individual cells. However, traditional methods of segmenting and measuring osteocyte connectomics are time-consuming and labour-intensive, often hindered by human subjectivity and limited throughput. In this study, we explore the application of deep learning and computer vision techniques to automate the segmentation and measurement of osteocyte connectomics, enabling more efficient and accurate analysis. We compare several state-of-the-art computer vision models (U-Nets and Vision Transformers) to successfully segment the LCN, finding that an Attention U-Net model can accurately segment and measure 81.8% of osteocytes and 42.1% of dendritic processes, when compared to manual labelling. While further development is required, we demonstrate that this degree of accuracy is already sufficient to distinguish between bones of young (2 month old) and aged (36 month old) mice, as well as capturing the degeneration induced by genetic modification of osteocytes. By harnessing the power of these advanced technologies, further developments can unravel the complexities of osteocyte networks in unprecedented detail, revolutionising our understanding of bone health and disease.

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Segmentation of nanotomographic cortical bone images for quantitative characterization of the osteoctyte lacuno-canalicular network

Cited by 4 publications

References 21 publications

Ptychographic X-ray CT characterization of the osteocyte lacuno-canalicular network in a male rat's glucocorticoid induced osteoporosis model

Ptychographic X-ray CT characterization of the osteocyte lacuno-canalicular network in a male rat's glucocorticoid induced osteoporosis model

New insights in osteocyte imaging by synchrotron radiation

Deep learning models to map osteocyte networks can successfully distinguish between young and aged bone

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