Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

Nagarajan, Mahesh; Checefsky, Walter A.; Abidin, Anas Z.; Tsai, Halley F.; Wang, Xixi; Hobbs, Susan K.; Bauer, Jan; Baum, Thomas; Wismüller, Axel

doi:10.1117/12.2082059

Cited by 7 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thirteen studies investigated bone properties such as vertebral fracture load, ( 23–25 ) microarchitecture parameters, ( 26,27 ) vertebral height, ( 28 ) or BMD ( 29–35 ) (Table 1). The main objective of these efforts was to improve the diagnosis of osteoporosis.…”

Section: Resultsmentioning

confidence: 99%

“…The main objective of these efforts was to improve the diagnosis of osteoporosis. Vertebral fracture load was assessed from donors ( 24,25 ) or using finite element analysis. ( 23 ) Microarchitecture parameters were determined using simulations or data collected from human cadavers.…”

Section: Resultsmentioning

confidence: 99%

“…( 23,32 ) Feature selection, ( 23 ) data augmentation, (32 ) and transfer learning from pre‐trained models ( 28,30 ) were applied to control overfitting. However, critical limitations in the use of ML methods were present: the gold standard, BMD as estimated from DXA, was not used as the ground truth (BMD from DXA is the reference technique used in practice, whereas BMD as estimated from CT scans was used as the ground truth); (30–32,34 ) model parameter selection was not reported; (23–25,29,33,35 ) discussion of crucial limitations regarding generalization was absent; (24,25,27,28,32,35 ) and the inclusion of the training set in model evaluation ( 33 ) or the use of 25 times more input features than data, ( 35 ) both indicating an increased risk of overfitting. Of note, Pang and colleagues reported their methods in detail and shared the code used for their analysis.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

115

View full text Add to dashboard Cite

Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12‐point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML‐based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model‐based decision‐making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

115

View full text Add to dashboard Cite

show abstract

“…Bone mass or bone mineral content is currently assessed most commonly via dual-energy x-ray absorptiometry (DXA) 1,2 . Over the years, cross-sectional imaging methods such as quantitative computed tomography (qCT) [3][4][5][6][7][8][9] and magnetic resonance imaging (MRI) [10][11][12][13][14] have been shown to provide useful additional clinical information beyond DXA secondary to their ability to image bone in 3-D and provide metrics of bone structure and quality 15 .…”

Section: Introductionmentioning

confidence: 99%

Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

Deniz

Xiang

Hallyburton

et al. 2018

Sci Rep

145

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.

show abstract

“…13,14,19 For volumetric data, grey-level co-occurrence matrix (GLCM) based texture analysis, first introduced by Haralick, 11,12 has been previously applied to medical CT imaging of bone. 8,20,22 Moreover, GLCM parameters have shown good correlations to the BV/TV and biomechanical properties of bone from lCT data when the volumetric data is re-projected in 2D. 30 GLCM-based textural analysis is a method to extract second order statistical features from grey-level images.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

et al. 2019

View full text Add to dashboard Cite

The aim of this study was to quantify subresolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R 2 change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM-and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

show abstract

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

Cited by 7 publications

References 35 publications

Machine Learning Solutions for Osteoporosis—A Review

Machine Learning Solutions for Osteoporosis—A Review

Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

Contact Info

Product

Resources

About