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Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice’s similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12–15 min, nearly an order of magnitude faster than the multi-atlas approach. [ABSTRACT FROM AUTHOR]

To develop an automated approach for 3D quantitative assessment and measurement of alpha angles from the femoral head-neck (FHN) junction using bone models derived from magnetic resonance (MR) images of the hip joint.Bilateral MR images of the hip joints were acquired from 30 male volunteers (healthy active individuals and high-performance athletes, aged 18–49 years) using a water-excited 3D dual echo steady state (DESS) sequence. In a subset of these subjects (18 water-polo players), additional True Fast Imaging with Steady-state Precession (TrueFISP) images were acquired from the right hip joint. For both MR image sets, an active shape model based algorithm was used to generate automated 3D bone reconstructions of the proximal femur. Subsequently, a local coordinate system of the femur was constructed to compute a 2D shape map to project femoral head sphericity for calculation of alpha angles around the FHN junction. To evaluate automated alpha angle measures, manual analyses were performed on anterosuperior and anterior radial MR slices from the FHN junction that were automatically reformatted using the constructed coordinate system.High intra- and inter-rater reliability (intra-class correlation coefficients  >  0.95) was found for manual alpha angle measurements from the auto-extracted anterosuperior and anterior radial slices. Strong correlations were observed between manual and automatic measures of alpha angles for anterosuperior (r  =  0.84) and anterior (r  =  0.92) FHN positions. For matched DESS and TrueFISP images, there were no significant differences between automated alpha angle measures obtained from the upper anterior quadrant of the FHN junction (two-way repeated measures ANOVA, F  <  0.01, p  =  0.98).Our automatic 3D method analysed MR images of the hip joints to generate alpha angle measures around the FHN junction circumference with very good reliability and reproducibility. This work has the potential to improve analyses of cam-type lesions of the FHN junction for large-scale morphometric and clinical MR investigations of the human hip region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate segmentation of hip joint cartilage from magnetic resonance (MR) images offers opportunities for quantitative investigations of pathoanatomical conditions such as osteoarthritis. In this paper, we present a fully automatic scheme for the segmentation of the individual femoral and acetabular cartilage plates in the human hip joint from high-resolution 3D MR images. The developed scheme uses an improved optimal multi-object multi-surface graph search framework with an arc-weighted graph representation that incorporates prior morphological knowledge as a basis for segmentation of the individual femoral and acetabular cartilage plates despite weak or incomplete boundary interfaces. This automated scheme was validated against manual segmentations from 3D true fast imaging with steady-state precession (TrueFISP) MR examinations of the right hip joints in 52 asymptomatic volunteers. Compared with expert manual segmentations of the combined, femoral and acetabular cartilage volumes, the automatic scheme obtained mean (± standard deviation) Dice’s similarity coefficients of 0.81 (± 0.03), 0.79 (± 0.03) and 0.72 (± 0.05). The corresponding mean absolute volume difference errors were 8.44% (± 6.36), 9.44% (± 7.19) and 9.05% (± 8.02). The mean absolute differences between manual and automated measures of cartilage thickness for femoral and acetabular cartilage plates were 0.13 mm (± 0.12) and 0.11 mm (± 0.11), respectively. [ABSTRACT FROM AUTHOR]