Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

Background Arthroscopic surgery for femoroacetabular impingement syndrome (FAI) is known to lead to self-reported symptom improvement. In the context of surgical interventions with known contextual effects and no true sham comparator trials, it is important to ascertain outcomes that are less susceptible to placebo effects. The primary aim of this trial was to determine if study participants with FAI who have hip arthroscopy demonstrate greater improvements in delayed gadolinium-enhanced magnetic resonance imaging (MRI) of cartilage (dGEMRIC) index between baseline and 12 months, compared to participants who undergo physiotherapist-led management. Methods Multi-centre, pragmatic, two-arm superiority randomised controlled trial comparing physiotherapist-led management to hip arthroscopy for FAI. FAI participants were recruited from participating orthopaedic surgeons clinics, and randomly allocated to receive either physiotherapist-led conservative care or surgery. The surgical intervention was arthroscopic FAI surgery. The physiotherapist-led conservative management was an individualised physiotherapy program, named Personalised Hip Therapy (PHT). The primary outcome measure was change in dGEMRIC score between baseline and 12 months. Secondary outcomes included a range of patient-reported outcomes and structural measures relevant to FAI pathoanatomy and hip osteoarthritis development. Interventions were compared by intention-to-treat analysis. Results Ninety-nine participants were recruited, of mean age 33 years and 58% male. Primary outcome data were available for 53 participants (27 in surgical group, 26 in PHT). The adjusted group difference in change at 12 months in dGEMRIC was -59 ms (95%CI − 137.9 to - 19.6) (p = 0.14) favouring PHT. Hip-related quality of life (iHOT-33) showed improvements in both groups with the adjusted between-group difference at 12 months showing a statistically and clinically important improvement in arthroscopy of 14 units (95% CI 5.6 to 23.9) (p = 0.003). Conclusion The primary outcome of dGEMRIC showed no statistically significant difference between PHT and arthroscopic hip surgery at 12 months of follow-up. Patients treated with surgery reported greater benefits in symptoms at 12 months compared to PHT, but these benefits are not explained by better hip cartilage metabolism. Trial registration details Australia New Zealand Clinical Trials Registry reference: ACTRN12615001177549. Trial registered 2/11/2015.

Highlights• Establish a standard framework with 25 manually annotated 3D T2MRI data for an objective comparison of intervertebral disc (IVD) localization and segmentation methods• Investigate strengths and limitations of a representative selection of the state-of-the-art IVD localization and segmentation methods with a challenge setup• Results achieved by the best algorithms in this study set new frontiers for IVD localization and segmentation from MR data Experimental results show that overall the best localization method achieves a mean localization distance of 0.8mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1mm and a mean Hausdorff distance of 4.3mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods.

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Conclusions 15The presented semi-automated method provides an alternative to time-and expertise-intensive 16 manual procedures for analysis of larger cross-sectional, interventional and longitudinal MR studies 17 for morphometric analyses of lumbar IVDs. 18

Combination of the novel 3D-based shape and signal intensity features on 3D (area under receiver operating curve (AUC) 0.984) and 2D (AUC 0.988) magnetic resonance data deliver a significant improvement in automated classification of IVD degeneration, compared to the combination of previously used 2D radiological measurement and signal intensity features (AUC 0.976 and 0.983, respectively). Further work is required regarding the usefulness of 2D and 3D shape data in relation to clinical scores of lower back pain. The results reveal the potential of the proposed informatics system for computer-aided IVD diagnosis from MRI in large-scale research studies and as a possible adjunct for clinical diagnosis.

Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.

We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926  ±  0.050 and 0.837  ±  0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806  ±  0.133 for the humerus and 0.795  ±  0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.