A new osteophyte segmentation algorithm using partial shape model and its applications to rabbit femur anterior cruciate ligament transection via micro-CT imaging

Empirical Modelling Workflow for Resolution Invariant Assessment of Osteophytes

OBJECTIVE

Traditional quantitative analysis of bone microstructure in micro-computed tomography (microCT) is dependent on animal scale and requires parametric tuning in new implementations. This study aims to develop an automated and resolution-invariant 3D image processing workflow for quantitative assessment of osteophytes.

METHODS

In this workflow, cortical bone was segmented from microCT scans, and a 3D sphere-fitting transform was performed to obtain a thickness map, for which each voxel is assigned a thickness value corresponding to the size of the largest sphere containing the voxel that fits entirely within the cortical bone. From the thickness map, a 1-voxel thick outer surface was extracted to model surface roughness. The thickness values of the outer surface were empirically estimated by a series of known statistical distributions. Resulting parameters describing best-fit distributions, along with other cortical bone metrics, were analysed to determine sensitivity to osteoarthritis and the presence of osteophytes.

RESULTS

The workflow was validated using microCT scans and histological gradings of rabbit and rat tibiofemoral joints. Visual inspection shows that samples with osteoarthritis and the presence of osteophytes have more surface voxels assigned small thickness values. The distribution of surface thickness values for each animal is best described by Gamma distributions, whose shape parameter is consistently sensitive to osteoarthritis and the presence of osteophytes.

CONCLUSION

Combining traditional image processing with empirical distribution fitting provides an automated, objective, and resolution-invariant workflow for osteophyte assessment.

SIGNIFICANCE

The proposed method is simple, yet elegant in its implementation, and can be readily used in new implementations.

Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions

BACKGROUND

Forty to fifty percent of women and 13%-22% of men experience an osteoporosis-related fragility fracture in their lifetimes. After the age of 50 years, the risk of hip fracture doubles in every 10 years. x-Ray based DXA is currently clinically used to diagnose osteoporosis and predict fracture risk. However, it provides only 2-D representation of bone and is associated with other technical limitations. Thus, alternative methods are needed.

PURPOSE

To develop and evaluate an ultra-low dose (ULD) hip CT-based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions.

METHODS

An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)-based computation of femur likelihood map followed by shape model-based femur segmentation and finite element analysis-based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT-based automated measurements of femoral vBMD.

RESULTS

Dice scores of accuracy (n = 20), reproducibility (n = 5), and generalizability (n = 12) of ULD CT-based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT-based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root-mean-square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT-derived reference measure. After 3-digit approximation, each of Pearson and concordance correlation coefficients as well as intraclass correlation coefficient (ICC) between baseline and repeat scans were 0.996 with RMSCV of 0.72%. Results of ULD CT-based bone analysis on 100 participants (age (mean ± SD) 73.6 ± 6.6 years) show that males have significantly greater (p < 0.01) vBMD at the femoral head and trochanteric regions than females, while females have moderately greater vBMD (p = 0.05) at the medial half of the femoral neck than males.

CONCLUSION

Deep learning, combined with shape model and finite element analysis, offers an accurate, reproducible, and generalizable algorithm for automated segmentation of the proximal femur and anatomic femoral subregions using ULD hip CT images. ULD CT-based regional measures of femoral vBMD are accurate and reproducible and demonstrate regional differences between males and females.

DCES-PA: Deformation-controllable elastic shape model for 3D bone proliferation analysis using hand HR-pQCT images

Bone proliferation is an important pathological feature of inflammatory rheumatic diseases. Although recent advance in high-resolution peripheral quantitative computed tomography (HR-pQCT) enables physicians to study microarchitectures, physicians' annotation of proliferation suffers from slice inconsistency and subjective variations. Also, there are only few effective automatic or semi-automatic tools for proliferation detection. In this study, by integrating pathological knowledge of proliferation formation with the advancement of statistical shape analysis theory, we present an unsupervised method, named Deformation-Controllable Elastic Shape model, for 3D bone Proliferation Analysis (DCES-PA). Unlike previous shape analysis methods that directly regularize the smoothness of the displacement field, DCES-PA regularizes the first and second-order derivative of the displacement field and decomposes these vector fields according to different deformations. For the first-order elastic metric, DCES-PA orthogonally decomposes the first-order derivative of the displacement field by shearing, scaling and bending deformation, and then penalize deformations triggering proliferation formation. For the second-order elastic metric, DCES-PA encodes both intrinsic and extrinsic surface curvatures into the second-order derivative of the displacement field to control the generation of high-curvature regions. By integrating the elastic shape metric with the varifold distances, DCES-PA achieves correspondence-free shape analysis. Extensive experiments on both simulated and real clinical datasets demonstrate that DCES-PA not only shows an improved accuracy than other state-of-the-art shape-based methods applied to proliferation analysis but also produces highly sensitive proliferation annotations to assist physicians in proliferation analysis.

Semi-automatic micro-CT segmentation of the midfoot using calibrated thresholds

PURPOSE

In the field of skeletal research, accurate and reliable segmentation methods are necessary for quantitative micro-CT analysis to assess bone quality. We propose a method of semi-automatic image segmentation of the midfoot, using the cuneiform bones as a model, based on thresholds set by phantom calibration that allows reproducible results in low cortical thickness bones.

METHODS

Manual and semi-automatic segmentation methods were compared in micro-CT scans of the medial and intermediate cuneiforms of 24 cadaveric specimens. The manual method used intensity thresholds, hole filling, and manual cleanup. The semi-automatic method utilized calibrated bone and soft tissue thresholds Boolean subtraction to cleanly identify edges before hole filling. Intra- and inter-rater reliability was tested for the semi-automatic method in all specimens. Mask volume and average bone mineral density (BMD) were measured for all masks, and the three-dimensional models were compared to the initial semi-automatic segmentation using an unsigned distance part comparison analysis. Segmentation methods were compared with paired t-tests with significance level 0.05, and reliability was analyzed by calculating intra-class correlation coefficients.

RESULTS

There were statistically significant differences in mask volume and BMD between the manual and semi-automatic segmentation methods in both bones. The intra- and inter-reliability was excellent for mask volume and bone density in both bones. Part comparisons showed a higher maximum distance between surfaces for the manual segmentation than the repeat semi-automatic segmentations.

CONCLUSION

We developed a semi-automatic micro-CT segmentation method based on calibrated thresholds. This method was designed specifically for use in bones with high rates of curvature and low cortical bone density, such as the cuneiforms, where traditional threshold-based segmentation is more challenging. Our method shows improvement over manual segmentation and was highly reliable, making it appropriate for use in quantitative micro-CT analysis.

Osteophyte volume calculation using dissimilarity-excluding Procrustes registration of archived bone models from healthy volunteers

Osteophytes are associated with later stage osteoarthritis and are most commonly described using semiquantitative radiographic grading systems. A detailed understanding of osteophyte formation is, in part, limited by the ability to quantify bone pathology. Osteophytes can be quantified relative to pre-osteoarthritic bone, or to the contralateral bone if it is healthy; however, in many cases, neither are available as references. We present a method for computing three-dimensional (3D) osteophyte models using a library of healthy control bones. An existing data set containing the computed tomography scans of 90 patients with first carpometacarpal osteoarthritis (OA) and 46 healthy subjects were utilized. A healthy bone that best fit each OA subject's bone was determined using a dissimilarity-excluding Procrustes registration technique (DEP) that minimized the influence of dissimilar features (ie, osteophytes). The osteophyte model was then computed through Boolean subtraction of the reference bone model from the OA bone model. DEP reference bones conformed significantly better to the OA bones (P < .0001) than by finite difference iterative closest point registration (root mean squared distances, 0.33 ± 0.05 and 0.41 ± 0.16 mm, respectively). The effect of library size on dissimilarity measure was investigated by leave-k-out cross-validation randomly reducing k from 46 to 1. A library of n ≥ 31 resulted in less than 10% difference from the theoretical minimum value. The proposed method enables quantification of osteophytes when the disease-free bone or the healthy contralateral bone is not available for any 3D data set. Quantifying osteophyte formation and growth may aid in understating the associated mechanisms in OA.

Spatial Distribution of Microcracks in Osteoarthritic Femoral Neck: Influence of Osteophytes on Microcrack Formation

Osteophytes have been suggested to influence the bone mechanical properties. The aim of this study was to compare the microcrack density in osteophytes with that in the other parts of the osteoarthritic femoral neck (FN). The presence of microcracks was investigated in the ultra-distal FN and in the osteophytes in samples obtained during hip arthroplasty in 24 postmenopausal women aged 67 ± 10 years. Furthermore, the 3D microarchitecture and the collagen crosslinks contents were assessed by high-resolution peripheral quantitative computed tomography and high-performance liquid chromatography, respectively. Osteophytes were present in the 24 FN, mainly at the level of the inferior quadrant. Microcracks were present in all FN with an average of 2.8 per sample. All observed microcracks were linear. The microcrack density (Cr.N/BV; #/mm²) was significantly higher in cancellous than in cortical bone (p = 0.004), whereas the microcrack length (Cr.Le, µm) was significantly greater in cortical bone (p = 0.04). The collagen crosslinks ratio pyridinoline/deoxypyridinoline was significantly and negatively correlated with Cr.N/BV in the posterior (r' = - 0.68, p = 0.01) and inferior (r' = - 0.53, p = 0.05) quadrants. Microcracks were observed in seven osteophytes in seven patients. When microcracks were present in the osteophyte area, Cr.N/BV was also significantly higher in the whole FN and in the quadrant of the osteophyte compared to the cases without microcrack in the osteophyte (p < 0.03). In conclusion, in FN from hip osteoarthritis microcracks are present in all FNs but in only 23% of the osteophytes. The microcrack formation was greater and their progression was smaller in the cancellous bone than in the cortex. The spatial distribution of microcracks varied according to the proximity of the osteophyte, and suggests that osteophyte may influence microcrack formation related to changes in local bone quality.

Automated assessment of bone changes in cross-sectional micro-CT studies of murine experimental osteoarthritis

OBJECTIVE

The degradation of articular cartilage, which characterises osteoarthritis (OA), is usually paired with excessive bone remodelling, including subchondral bone sclerosis, cysts, and osteophyte formation. Experimental models of OA are widely used to investigate pathogenesis, yet few validated methodologies for assessing periarticular bone morphology exist and quantitative measurements are limited by manual segmentation of micro-CT scans. The aim of this work was to chart the temporal changes in periarticular bone in murine OA by novel, automated micro-CT methods.

METHODS

OA was induced by destabilisation of the medial meniscus (DMM) in 10-week old male mice and disease assessed cross-sectionally from 1- to 20-weeks post-surgery. A novel approach was developed to automatically segment subchondral bone compartments into plate and trabecular bone in micro-CT scans of tibial epiphyses. Osteophyte volume, as assessed by shape differences using 3D image registration, and by measuring total epiphyseal volume was performed.

RESULTS

Significant linear and volumetric structural modifications in subchondral bone compartments and osteophytes were measured from 4-weeks post-surgery and showed progressive changes at all time points; by 20 weeks, medial subchondral bone plate thickness increased by 160±19.5 μm and the medial osteophyte grew by 0.124±0.028 μm3. Excellent agreement was found when automated measurements were compared with manual assessments.

CONCLUSION

Our automated methods for assessing bone changes in murine periarticular bone are rapid, quantitative, and highly accurate, and promise to be a useful tool in future preclinical studies of OA progression and treatment. The current approaches were developed specifically for cross-sectional micro-CT studies but could be applied to longitudinal studies.

Comparing radial styloid size between osteoarthritic and healthy wrists: a pathoanatomical three-dimensional study

Radial styloid pointing due to spur formation is considered an early sign of osteoarthritis, but is sometimes difficult to distinguish from normal anatomic variation. In this pathoanatomical study we used three-dimensional imaging techniques to evaluate quantitatively whether the styloid size is larger in wrists with scaphoid non-union than in healthy wrists. We compared these findings with duration of the non-union and with the scaphoid non-union advanced collapse classification, which was based on radiographic assessment of the general level of wrist osteoarthritis. In 31 patients, the injured styloid was consistently larger than the contralateral healthy styloid. In 74% of the patients this pathoanatomical difference (maximum 5 mm) exceeded anatomical left-to-right variation in styloid size (maximum 2 mm), indicating significant pointing. Increased styloid pointing was associated with older non-unions, and with more severe osteoarthritis. Three-dimensional styloid pointing analysis is an objective method to assess osteoarthritic progression. Combining traditional qualitative evaluation and quantitative measurements may improve the classification of wrist osteoarthritis.

LEVEL OF EVIDENCE

IV.

Analysis of deformity in scaphoid non-unions using two- and three-dimensional imaging

Pre-operative assessment of the deformity in scaphoid non-unions influences surgical decision-making. To characterize deformity, we used three-dimensional computed tomographic modelling in 28 scaphoid non-unions, and quantified bone loss, dorsal osteophyte volume and flexion deformity. We further related these three-dimensional parameters to the intrascaphoid and capitate-lunate angles, and stage of scaphoid non-union advanced collapse assessed on conventional two-dimensional images and to the chosen surgical procedure. Three-dimensional flexion deformity (mean 26°) did not correlate with intrascaphoid and capitate-lunate angles. Osteophyte volume was positively correlated with bone loss and stage of scaphoid non-union advanced collapse. Osteophyte volume and bone loss increased over time. Three-dimensional modelling enables the quantification of bone loss and osteophyte volume, which may be valuable parameters in the characterization of deformity and subsequent decision-making about treatment, when taken in addition to the clinical aspects and level of osteoarthritis.

TYPE OF STUDY/LEVEL OF EVIDENCE

Level IV.

Digital Topology and Geometry in Medical Imaging: A Survey

Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.

Compounding local invariant features and global deformable geometry for medical image registration

Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features and global deformable geometry. This model has four steps. First, a set of highly-repeatable and highly-robust local invariant features, called Key Features Model (KFM), are extracted by an effective matching strategy. Second, local features can be matched more accurately through the KFM for the purpose of initializing a global deformable model. Third, the positional relationship between the KFM and the global deformable model can be used to precisely pinpoint all landmarks after initialization. And fourth, the final pose of the global deformable model is determined by an iterative process with a lower time cost. Through the practical experiments, the paper finds three important conclusions. First, it proves that the KFM can detect the matching feature points well. Second, the precision of landmark locations adjusted by the modeled relationship between KFM and global deformable model is greatly improved. Third, regarding the fitting accuracy and efficiency, by observation from the practical experiments, it is found that the proposed method can improve % of the fitting accuracy and reduce around 50% of the computational time compared with state-of-the-art methods.

Computed tomography of subchondral bone and osteophytes in hip osteoarthritis: the shape of things to come?

Bone is a fundamental component of the disordered joint homeostasis seen in osteoarthritis, a disease that has been primarily characterized by the breakdown of articular cartilage accompanied by local bone changes and a limited degree of joint inflammation. In this review we consider the role of computed tomography imaging and computational analysis in osteoarthritis research, focusing on subchondral bone and osteophytes in the hip. We relate what is already known in this area to what could be explored through this approach in the future in relation to both clinical research trials and the underlying cellular and molecular science of osteoarthritis. We also consider how this area of research could impact on our understanding of the genetics of osteoarthritis.