Three-dimensional hip cartilage quality assessment of morphology and dGEMRIC by planar maps and automated segmentation

A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography

The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs with a U-Net architecture were used to develop a fully automated segmentation model for hip cartilage and labrum from MRI. Direct hip MR arthrographies (01/2020-10/2021) were selected from 100 symptomatic patients. Institutional routine protocol included a 3D T1 mapping sequence, which was used for manual segmentation of hip cartilage and labrum. 80 hips were used for training and the remaining 20 for testing. Model performance was assessed with six evaluation metrics including Dice similarity coefficient (DSC). In addition, model performance was tested on an external dataset (40 patients) with a 3D T2-weighted sequence from a different institution. Inter-rater agreement of manual segmentation served as benchmark for automatic segmentation performance. 100 patients were included (mean age 30 ± 10 years, 64% female patients). Mean DSC for cartilage was 0.92 ± 0.02 (95% confidence interval [CI] 0.92-0.93) and 0.83 ± 0.04 (0.81-0.85) for labrum and comparable (p = 0.232 and 0.297, respectively) to inter-rater agreement of manual segmentation: DSC cartilage 0.93 ± 0.04 (0.92-0.95); DSC labrum 0.82 ± 0.05 (0.80-0.85). When tested on the external dataset, the DSC was 0.89 ± 0.02 (0.88-0.90) and 0.71 ± 0.04 (0.69-0.73) for cartilage and labrum, respectively.The presented deep learning approach accurately segments hip cartilage and labrum from 3D MRI sequences and can potentially be used in clinical practice to provide rapid and accurate 3D MRI models.

3.0T magnetic resonance imaging-based hip bone models for femoroacetabular impingement syndrome are equivalent to computed tomography-based models

This study aimed to compare three-dimensional (3D) proximal femoral and acetabular surface models generated from 3.0T magnetic resonance imaging (MRI) to the clinical gold standard of computed tomography (CT). Ten intact fresh-frozen cadaveric hips underwent CT and 3.0T MRI scans. The CT- and MRI-based segmented models were superimposed using a validated 3D-3D registration volume-merge method to compare them. The least surface-to-surface distance between the models was calculated by a point-to-surface calculation algorithm using a custom-written program. The variables of interest were the signed and absolute surface-to-surface distance between the paired bone models. One-sample t-tests were performed using a signed and absolute test value of 0.16 mm and 0.37 mm, respectively, based on a previous study that validated 1.5T MRI bone models by comparison with CT bone models. For the femur, the average signed and absolute surface-to-surface distance was 0.18 ± 0.09 mm and 0.30 ± 0.06 mm, respectively. There was no difference in the signed surface-to-surface distance and the 0.16 mm test value (t = 0.650, p = 0.532). However, the absolute surface-to-surface difference was less than the 0.37 mm test value (t = -4.025, p = 0.003). For the acetabulum, the average signed and absolute surface-to-surface distance was -0.06 ± 0.06 mm and 0.26 ± 0.04 mm, respectively. The signed (t = -12.569, p < 0.001) and absolute (t = -8.688, p < 0.001) surface-to-surface difference were less than the 0.16 mm and 0.37 mm test values, respectively. Our data shows that 3.0T MRI bone models are more similar to CT bone models than previously validated 1.5T MRI bone models. This is likely due to the higher resolution of the 3T data.

Automated quantification of cartilage quality for hip treatment decision support

Purpose

Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools.

Methods

A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume.

Results

The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed.

Conclusion

An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future.

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification

Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landmark-based detection and quantification of hip abnormalities from magnetic resonance (MR) images. The framework relies on a novel idea of multi-landmark environment analysis with reinforcement learning. In particular, we merge the concepts of the graphical lasso and Morris sensitivity analysis with deep neural networks to quantitatively estimate the contribution of individual landmark and landmark subgroup locations to the other landmark locations. Convolutional neural networks for image segmentation are utilized to propose the initial landmark locations, and landmark detection is then formulated as a reinforcement learning (RL) problem, where each landmark-agent can adjust its position by observing the local MR image neighborhood and the locations of the most-contributive landmarks. The framework was validated on T1-, T2- and proton density-weighted MR images of 260 patients with the aim to measure the lateral center-edge angle (LCEA), femoral neck-shaft angle (NSA), and the anterior and posterior acetabular sector angles (AASA and PASA) of the hip, and derive the quantitative abnormality metrics from these angles. The framework was successfully tested using the UNet and feature pyramid network (FPN) segmentation architectures for landmark proposal generation, and the deep Q-network (DeepQN), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and actor-critic policy gradient (A2C) RL networks for landmark position optimization. The resulting overall landmark detection error of 1.5 mm and angle measurement error of 1.4° indicates a superior performance in comparison to existing methods. Moreover, the automatically estimated abnormality labels were in 95% agreement with those generated by an expert radiologist.

Entropy and distance maps-guided segmentation of articular cartilage: data from the Osteoarthritis Initiative

PURPOSE

Accurate segmentation of articular cartilage from MR images is crucial for quantitative investigation of pathoanatomical conditions such as osteoarthritis (OA). Recently, deep learning-based methods have made significant progress in hard tissue segmentation. However, it remains a challenge to develop accurate methods for automatic segmentation of articular cartilage.

METHODS

We propose a two-stage method for automatic segmentation of articular cartilage. At the first stage, nnU-Net is employed to get segmentation of both hard tissues and articular cartilage. Based on the initial segmentation, we compute distance maps as well as entropy maps, which encode the uncertainty information about the initial cartilage segmentation. At the second stage, both distance maps and entropy maps are concatenated to the original image. We then crop a sub-volume around the cartilage region based on the initial segmentation, which is used as the input to another nnU-Net for segmentation refinement.

RESULTS

We designed and conducted comprehensive experiments on segmenting three different types of articular cartilage from two datasets, i.e., an in-house dataset consisting of 25 hip MR images and a publicly available dataset from Osteoarthritis Initiative (OAI). Our method achieved an average Dice similarity coefficient (DSC) of [Formula: see text] for the combined hip cartilage, [Formula: see text] for the femoral cartilage and [Formula: see text] for the tibial cartilage, respectively.

CONCLUSION

In summary, we developed a new approach for automatic segmentation of articular cartilage from MR images. Comprehensive experiments conducted on segmenting articular cartilage of the knee and hip joints demonstrated the efficacy of the present approach. Our method achieved equivalent or better results than the state-of-the-art methods.

Automatic Cartilage Segmentation for Delayed Gadolinium-Enhanced Magnetic Resonance Imaging of Hip Joint Cartilage: A Feasibility Study

OBJECTIVE

Automatic segmentation for biochemical cartilage evaluation holds promise for an efficient and reader-independent analysis. This pilot study aims to investigate the feasibility and to compare delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) hip joint assessment with manual segmentation of acetabular and femoral head cartilage and dGEMRIC hip joint assessment using automatic surface and volume processing software at 3 Tesla.

DESIGN

Three-dimensional (3D) dGEMRIC data sets of 6 patients with hip-related pathology were assessed (1) manually including multiplanar image reformatting and regions of interest (ROI) analysis and (2) automated by using a combined surface and volume processing software. For both techniques, T1_Gd values were obtained in acetabular and femoral head cartilage at 7 regions (anterior, anterior-superior, superior-anterior, superior, superior-posterior, posterior-superior, and posterior) in central and peripheral portions. Correlation between both techniques was calculated utilizing Spearman's rank correlation coefficient.

RESULTS

A high correlation between both techniques was observed for acetabular (ρ = 0.897; P < 0.001) and femoral head (ρ = 0.894; P < 0.001) cartilage in all analyzed regions of the hip joint (ρ between 0.755 and 0.955; P < 0.001).

CONCLUSIONS

Automatic cartilage segmentation with dGEMRIC assessment for hip joint cartilage evaluation seems feasible providing high to excellent correlation with manually performed ROI analysis. This technique is feasible for an objective, reader-independant and reliable assessment of biochemical cartilage status.

Automatic MRI-based Three-dimensional Models of Hip Cartilage Provide Improved Morphologic and Biochemical Analysis

BACKGROUND

The time-consuming and user-dependent postprocessing of biochemical cartilage MRI has limited the use of delayed gadolinium-enhanced MRI of cartilage (dGEMRIC). An automated analysis of biochemical three-dimensional (3-D) images could deliver a more time-efficient and objective evaluation of cartilage composition, and provide comprehensive information about cartilage thickness, surface area, and volume compared with manual two-dimensional (2-D) analysis.

QUESTIONS/PURPOSES

(1) How does the 3-D analysis of cartilage thickness and dGEMRIC index using both a manual and a new automated method compare with the manual 2-D analysis (gold standard)? (2) How does the manual 3-D analysis of regional patterns of dGEMRIC index, cartilage thickness, surface area and volume compare with a new automatic method? (3) What is the interobserver reliability and intraobserver reproducibility of software-assisted manual 3-D and automated 3-D analysis of dGEMRIC indices, thickness, surface, and volume for two readers on two time points?

METHODS

In this IRB-approved, retrospective, diagnostic study, we identified the first 25 symptomatic hips (23 patients) who underwent a contrast-enhanced MRI at 3T including a 3-D dGEMRIC sequence for intraarticular pathology assessment due to structural hip deformities. Of the 23 patients, 10 (43%) were male, 16 (64%) hips had a cam deformity and 16 (64%) hips had either a pincer deformity or acetabular dysplasia. The development of an automated deep-learning-based approach for 3-D segmentation of hip cartilage models was based on two steps: First, one reader (FS) provided a manual 3-D segmentation of hip cartilage, which served as training data for the neural network and was used as input data for the manual 3-D analysis. Next, we developed the deep convolutional neural network to obtain an automated 3-D cartilage segmentation that we used as input data for the automated 3-D analysis. For actual analysis of the manually and automatically generated 3-D cartilage models, a dedicated software was developed. Manual 2-D analysis of dGEMRIC indices and cartilage thickness was performed at each "full-hour" position on radial images and served as the gold standard for comparison with the corresponding measurements of the manual and the automated 3-D analysis. We measured dGEMRIC index, cartilage thickness, surface area, and volume for each of the four joint quadrants and compared the manual and the automated 3-D analyses using mean differences. Agreement between the techniques was assessed using intraclass correlation coefficients (ICC). The overlap between 3-D cartilage volumes was assessed using dice coefficients and means of all distances between surface points of the models were calculated as average surface distance. The interobserver reliability and intraobserver reproducibility of the software-assisted manual 3-D and the automated 3-D analysis of dGEMRIC indices, thickness, surface and volume was assessed for two readers on two different time points using ICCs.

RESULTS

Comparable mean overall difference and almost-perfect agreement in dGEMRIC indices was found between the manual 3-D analysis (8 ± 44 ms, p = 0.005; ICC = 0.980), the automated 3-D analysis (7 ± 43 ms, p = 0.015; ICC = 0.982), and the manual 2-D analysis.Agreement for measuring overall cartilage thickness was almost perfect for both 3-D methods (ICC = 0.855 and 0.881) versus the manual 2-D analysis. A mean difference of -0.2 ± 0.5 mm (p < 0.001) was observed for overall cartilage thickness between the automated 3-D analysis and the manual 2-D analysis; no such difference was observed between the manual 3-D and the manual 2-D analysis.Regional patterns were comparable for both 3-D methods. The highest dGEMRIC indices were found posterosuperiorly (manual: 602 ± 158 ms; p = 0.013, automated: 602 ± 158 ms; p = 0.012). The thickest cartilage was found anteroinferiorly (manual: 5.3 ± 0.8 mm, p < 0.001; automated: 4.3 ± 0.6 mm; p < 0.001). The smallest surface area was found anteroinferiorly (manual: 134 ± 60 mm; p < 0.001, automated: 155 ± 60 mm; p < 0.001). The largest volume was found anterosuperiorly (manual: 2343 ± 492 mm; p < 0.001, automated: 2294 ± 467 mm; p < 0.001). Mean average surface distance was 0.26 ± 0.13 mm and mean Dice coefficient was 86% ± 3%. Intraobserver reproducibility and interobserver reliability was near perfect for overall analysis of dGEMRIC indices, thickness, surface area, and volume (ICC range, 0.962-1).

CONCLUSIONS

The presented deep learning approach for a fully automatic segmentation of hip cartilage enables an accurate, reliable and reproducible analysis of dGEMRIC indices, thickness, surface area, and volume. This time-efficient and objective analysis of biochemical cartilage composition and morphology yields the potential to improve patient selection in femoroacetabular impingement (FAI) surgery and to aid surgeons with planning of acetabuloplasty and periacetabular osteotomies in pincer FAI and hip dysplasia. In addition, this validation paves way to the large-scale use of this method for prospective trials which longitudinally monitor the effect of reconstructive hip surgery and the natural course of osteoarthritis.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Segmentation of hip cartilage in compositional magnetic resonance imaging: A fast, accurate, reproducible, and clinically viable semi-automated methodology

Manual segmentation is a significant obstacle in the analysis of compositional MRI for clinical decision-making and research. Our aim was to produce a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI. We produced a semi-automated segmentation method for cartilage segmentation of hip MRI sequences consisting of a two step process: (i) fully automated hierarchical partitioning of the data volume generated using a bespoke segmentation approach applied recursively, followed by (ii) user selection of the regions of interest using a region editor. This was applied to dGEMRIC scans at 3T taken from a prospective longitudinal study of individuals considered at high-risk of developing osteoarthritis (SibKids) which were also manually segmented for comparison. Fourteen hips were segmented both manually and using our semi-automated method. Per hip, processing time for semi-automated and manual segmentation was 10-15, and 60-120 min, respectively. Accuracy and Dice similarity coefficient (DSC) for the comparison of semi-automated and manual segmentations was 0.9886 and 0.8803, respectively. Intra-observer and inter-observer reproducibility of the semi-automated segmentation method gave an accuracy of 0.9997 and 0.9991, and DSC of 0.9726 and 0.9354, respectively. We have proposed a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI sequences. This enables accurate anatomical and biochemical measurements to be obtained quickly and reproducibly. This is the first such method that shows clinical applicability, and could have large ramifications for the use of compositional MRI in research and clinically. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Imaging of femoroacetabular impingement-current concepts

Following the recognition of femoroacetabular impingement (FAI) as a clinical entity, diagnostic tools have continuously evolved. While the diagnosis of FAI is primarily made based on the patients' history and clinical examination, imaging of FAI is indispensable. Routine diagnostic work-up consists of a set of plain radiographs, magnetic resonance imaging (MRI) and MR-arthrography. Recent advances in MRI technology include biochemically sensitive sequences bearing the potential to detect degenerative changes of the hip joint at an early stage prior to their appearance on conventional imaging modalities. Computed tomography may serve as an adjunct. Advantages of CT include superior bone to soft tissue contrast, making CT applicable for image-guiding software tools that allow evaluation of the underlying dynamic mechanisms causing FAI. This article provides a summary of current concepts of imaging in FAI and a review of the literature on recent advances, and their application to clinical practice.

Longitudinal study using voxel-based relaxometry: Association between cartilage T1ρ and T2 and patient reported outcome changes in hip osteoarthritis

PURPOSE

To study the local distribution of hip cartilage T_1ρ and T₂ relaxation times and their association with changes in patient reported outcome measures (PROMs) using a fully automatic, local, and unbiased method in subjects with and without hip osteoarthritis (OA).

MATERIALS AND METHODS

The 3 Tesla MRI studies of the hip were obtained for 37 healthy controls and 16 subjects with radiographic hip OA. The imaging protocol included a three-dimensional (3D) SPGR sequence and a combined 3D T_1ρ and T₂ sequence. Quantitative cartilage analysis was compared between a traditional region of interest (ROI)-based method and a fully automatic voxel-based relaxometry (VBR) method. Additionally, VBR was used to assess local T_1ρ and T₂ differences between subjects with and without OA, and to evaluate the association between T_1ρ and T₂ and 18-month changes PROMs.

RESULTS

Results for the two methods were consistent in the acetabular (R = 0.79; coefficients of variation [CV] = 2.9%) and femoral cartilage (R = 0.90; CV = 2.6%). VBR revealed local patterns of T_1ρ and T₂ elevation in OA subjects, particularly in the posterosuperior acetabular cartilage (T_1ρ : P = 0.02; T₂ : P = 0.038). Overall, higher T_1ρ and T₂ values at baseline, particularly in the anterosuperior acetabular cartilage (T_1ρ : Rho = -0.42; P = 0.002; T₂ : Rho = -0.44; P = 0.002), were associated with worsening PROMS at 18-month follow-up.

CONCLUSION

VBR is an accurate and robust method for quantitative MRI analysis in hip cartilage. VBR showed the capability to detect local variations in T_1ρ and T₂ values in subjects with and without osteoarthritis, and voxel based correlations demonstrated a regional dependence between baseline T_1ρ and T₂ values and changes in PROMs.

LEVEL OF EVIDENCE

1 J. MAGN. RESON. IMAGING 2017;45:1523-1533.

Segmentation of joint and musculoskeletal tissue in the study of arthritis

As the most frequent cause of physical disability, musculoskeletal diseases such as arthritis and osteoporosis have a great social and economical impact. Quantitative magnetic resonance imaging (MRI) biomarkers are important tools that allow clinicians to better characterize, monitor, and even predict musculoskeletal disease progression. Post-processing pipelines often include image segmentation. Manually identifying the border of the region of interest (ROI) is a difficult and time-consuming task. Manual segmentation is also affected by inter- and intrauser variability, thus limiting standardization. Fully automatic or semi-automatic methods that minimize the user interaction are highly desirable. Unfortunately, an ultimate, highly reliable and extensively evaluated solution for joint and musculoskeletal tissue segmentation has not yet been proposed, and many clinical studies still adopt fully manual procedures. Moreover, the clinical translation of several promising quantitative MRI techniques is highly affected by the lack of an established, fast, and accurate segmentation method. The goal of this review is to present some of the techniques proposed in recent literature that have been adopted in clinical studies for joint and musculoskeletal tissue analyses in arthritis patients. The most widely used MRI sequences and image processing algorithms employed to accomplish segmentation challenges will be discussed in this paper.

Planar dGEMRIC Maps May Aid Imaging Assessment of Cartilage Damage in Femoroacetabular Impingement

BACKGROUND

Three-dimensional (3-D) delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) helps quantify biochemical changes in articular cartilage that correlate with early-stage osteoarthritis. However, dGEMRIC analysis is performed slice by slice, limiting the potential of 3-D data to give an overall impression of cartilage biochemistry. We previously developed a computational algorithm to produce unfolded, or "planar," dGEMRIC maps of acetabular cartilage, but have neither assessed their application nor determined whether MRI-based grading of cartilage damage or dGEMRIC measurements predict intraoperative findings in hips with symptomatic femoroacetabular impingement (FAI).

QUESTIONS/PURPOSES

(1) Does imaging-based assessment of acetabular cartilage damage correlate with intraoperative findings in hips with symptomatic FAI? (2) Does the planar dGEMRIC map improve this correlation? (3) Does the planar map improve the correlation between the dGEMRIC index and MRI-based grading of cartilage damage in hips with symptomatic FAI? (4) Does the planar map improve imaging-based evaluation time for hips with symptomatic FAI?

METHODS

We retrospectively studied 47 hips of 45 patients with symptomatic FAI who underwent hip surgery between 2009 and 2013 and had a 1.5-T 3-D dGEMRIC scan within 6 months preoperatively. Our cohort included 25 males and 20 females with a mean ± SD age at surgery of 29 ± 11 years. Planar dGEMRIC maps were generated from isotropic, sagittal oblique TrueFISP and T1 sequences. A pediatric musculoskeletal radiologist with experience in hip MRI evaluated studies using radially reformatted sequences. For six acetabular subregions (anterior-peripheral [AP]; anterior-central [AC]; superior-peripheral [SP]; superior-central [SC]; posterior-peripheral [PP]; posterior-central [PC]), modified Outerbridge cartilage damage grades were recorded and region-of-interest T1 averages (the dGEMRIC index) were measured. Beck's intraoperative cartilage damage grades were compared with the Outerbridge grades and dGEMRIC indices. For a subset of 26 hips, 13 were reevaluated with the map and 13 without the map, and total evaluation times were recorded.

RESULTS

There were no meaningful differences in the correlations obtained with versus without referencing the planar maps. Planar map-independent Outerbridge grades had a notable (p < 0.05) Spearman's rank correlation (ρ) with Beck's grades that was moderate in AP, SC, and PC (0.3 < ρ < 0.5) and strong in SP (ρ > 0.5). For map-dependent Outerbridge grades, ρ was moderate in AP, AC, and SC and strong in SP. Map-independent dGEMRIC indices had a ρ with Beck's grades that was moderate in AP and SC (-0.3 > ρ > -0.5) and strong in SP (ρ < -0.5). For map-dependent dGEMRIC indices, ρ was moderate in SC and strong in SP. Similarly, there were no meaningful, map-dependent differences in the correlations. When comparing Outerbridge grades and dGEMRIC indices, there were notable correlations across all subregions. Without the planar map, ρ was moderate in AC and PC and strong in AP, SP, SC, and PP. With the map, ρ was strong in all six subregions. In AC, there was a notable map-dependent improvement in this correlation (p < 0.001). Finally, referencing the planar dGEMRIC map during evaluation was associated with a decrease in mean evaluation time, from 207 ± 32 seconds to 152 ± 33 seconds (p = 0.001).

CONCLUSIONS

Our work challenges the weak correlation between dGEMRIC and intraoperative findings of cartilage damage that was previously reported in hips with symptomatic FAI, suggesting that dGEMRIC has potential diagnostic use for this patient population. The planar dGEMRIC maps did not meaningfully alter the correlation of imaging-based evaluation of cartilage damage with intraoperative findings; however, they notably improved the correlation of dGEMRIC and MRI-based grading in AC, and their use incurred no additional time cost to imaging-based evaluation. Therefore, the planar maps may improve dGEMRIC's use as a continuous proxy for an otherwise discrete and simplified MRI-based grade of cartilage damage in hips with symptomatic FAI.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Prenatal nicotine exposure induces poor articular cartilage quality in female adult offspring fed a high-fat diet and the intrauterine programming mechanisms

Prenatal nicotine exposure (PNE) induces skeletal growth retardation and dyslipidemia in offspring displaying intrauterine growth retardation (IUGR). Cholesterol accumulation resulting from cholesterol efflux dysfunction may reduce the quality of articular cartilage through fetal programming. This study evaluated the quality of articular cartilage of female adult offspring fed a high-fat diet and explored the mechanisms using a rat IUGR model established by the administration of 2.0mg/kg/d of subcutaneous nicotine from gestational days 11-20. The results demonstrated an increased OARSI (Osteoarthritis Research Society International) score and total cholesterol content, decreased serum corticosterone, and increased IGF1 and dyslipidemia with catch-up growth in PNE adult offspring. Cartilage matrix, IGF1 and cholesterol efflux pathway expression were reduced in PNE fetuses and adult offspring. Therefore, PNE induced poor articular cartilage quality in female adult offspring fed a high-fat diet via a dual programming mechanism.

Evaluation of Automated Volumetric Cartilage Quantification for Hip Preservation Surgery

Automating the process of femoroacetabular cartilage identification from magnetic resonance imaging (MRI) images has important implications to guiding clinical care by providing a temporal metric that allows for optimizing the timing for joint preservation surgery. In this paper, we evaluate a new automated cartilage segmentation method using a time trial, segmented volume comparison, overlap metrics, and Euclidean distance mapping. We report interrater overlap metrics using the true fast imaging with steady-state precession MRI sequence of 0.874, 0.546, and 0.704 for the total overlap, union overlap, and mean overlap, respectively. This method was 3.28× faster than manual segmentation. This technique provides clinicians with volumetric cartilage information that is useful for optimizing the timing for joint preservation procedures.

Shape-based acetabular cartilage segmentation: application to CT and MRI datasets

PURPOSE

A new method for acetabular cartilage segmentation in both computed tomography (CT) arthrography and magnetic resonance imaging (MRI) datasets with leg tension is developed and tested.

METHODS

The new segmentation method is based on the combination of shape and intensity information. Shape information is acquired according to the predictable nonlinear relationship between the U-shaped acetabulum region and acetabular cartilage. Intensity information is obtained from the acetabular cartilage region automatically to complete the segmentation procedures. This method is evaluated using 54 CT arthrography datasets with two different radiation doses and 20 MRI datasets. Additionally, the performance of this method in identifying acetabular cartilage is compared with four other acetabular cartilage segmentation methods.

RESULTS

This method performed better than the comparison methods. Indeed, this method maintained good accuracy level for 74 datasets independent of the cartilage modality and with minimum user interaction in the bone segmentation procedures. In addition, this method was efficient in noisy conditions and in detection of the damaged cartilages with zero thickness, which confirmed its potential clinical usefulness.

CONCLUSIONS

Our new method proposes acetabular cartilage segmentation in three different datasets based on the combination of the shape and intensity information. This method executes well in situations where there are clear boundaries between the acetabular and femoral cartilages. However, the acetabular cartilage and pelvic bone information should be obtained from one dataset such as CT arthrography or MRI datasets with leg traction.