Automated analysis of hip joint cartilage combining MR T2 and three‐dimensional fast‐spin‐echo images

Chandra, Shekhar S.; Surowiec, Rachel K.; Ho, Charles P.; Xia, Ying; Engstrom, Craig; Crozier, Stuart; Fripp, Jürgen

doi:10.1002/mrm.25598

Cited by 17 publications

(27 citation statements)

References 43 publications

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“…(Used with permission from (Chandra et al 2015b;Ho et al 2014) 6.3 Cartilage thickness measurement illustrated on an example 2D slice overlaid with manual cartilage contours. The example slice is assumed to go through the estimated center of the femoral head.…”

Section: Declaration By Authormentioning

confidence: 99%

“…Li et al (2008) reported semi-automatic segmentation of the individual acetabular and femoral cartilage plates (using active contours) in 2D MEDIC MR images without the use of continuous leg traction. The use of these manual and semi-automatic approaches for cartilage segmentation typically requires skilled analysts with considerable experience and is time-consuming, e.g., 1 hour per image for semi-automatic analysis (Naish et al 2006) and 3 hours for manual delineation of the cartilages on each SPACE image (Chandra et al 2015b); consequently the development of an accurate automatic scheme for hip joint cartilage segmentation would be beneficial for large-scale clinical and research studies focusing on morphometric evaluations.…”

Section: Manual and Semi-automatic Segmentation Of Hip Joint Cartilagementioning

confidence: 99%

“…Another MR dataset provided by the Steadman Philippon Research Institute (SPRI) (Vail, Colorado, USA) as per Chandra et al (2015b);Ho et al (2014), was used in this chapter to obtain the prior information of clinically-defined anatomical subregions for the individual femoral and acetabular cartilage plates. This dataset consists of 24 anonymized volunteers (12 males and 12 females, aged 23-34 years, body masses 53-151 kg) enrolled in an asymptomatic study after subjective scoring, objective clinical examinations and radiological evaluation using the Hip Osteoarthritis MRI Scoring System (HOAMS) (Roemer et al 2011).…”

Section: Spri Clinical Space and T2 Mapping Imagesmentioning

confidence: 99%

“…In this chapter, the center of the femoral head is firstly estimated using a joint locator (Nishii et al 2004) and serves as a reference landmark for measuring the cartilage thickness at each vertex of the BCI surface, which aims to mitigate PVE since the cartilage surface is intersected perpendicularly with the line from the femoral Figure 6.2: (a) Partitioned regions with associated regional descriptors for the (top) acetabular and (bottom) femoral cartilages of the hip joint based on manual landmarks (red dots), which were illustrated using a 3D innominate bone volume from the left-side hip joint and a bone volume of proximal femur from the right-side hip joint, respectively; (b) Example manual cartilage segmentation overlaid on both the (left) 3D SPACE and (right) T2 echo as slices of the 3D image volume. (Used with permission from (Chandra et al 2015b;Ho et al 2014)) head center (Mechlenburg et al 2007). At each BCI vertex, a one-directional grey-value profile with the sampling spacing of 0.125 mm is extracted from the voxelization of cartilage segmentations along the direction from the femoral head center to this vertex (see Figure 6.3).…”

Section: Cartilage Thicknessmentioning

confidence: 99%

“…The disadvantages of these manual and semi-automatic approaches are that they are time-consuming (e.g., 1 hour per image for the livewire-based semi-automatic method (Naish et al 2006), 3 hours per SPACE scan for manual cartilage segmentation (Chandra et al 2015b)) and require skilled operators with significant experience. These would become problematic for potential research and clinical application in large prospective early and pre-OA studies to evaluate potential imaging biomarkers.…”

Section: Introductionmentioning

confidence: 99%

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Automated segmentation and quantitative analysis of the hip joint from magnetic resonance images

Xia¹

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The aim of this thesis is to develop a novel computer-aided system with advanced medical image processing approaches. This will allow automatic segmentation and quantification of the osteochondral elements (i.e. the articulating bones and cartilages) from high-resolution three-dimensional (3D) magnetic resonance (MR) images of the hip joint.This research is motivated by the importance of early detection of structural changes and degeneration of the bones and articular cartilages for good patient outcomes, particularly for early and pre-osteoarthritic conditions such as cam-type femoroacetabular impingement (FAI). MR imaging provides an optimal tool for in vivo assessment of the hip joint structure, including the bones and cartilages. This has generated extensive interest in the development of MR technologies to analyse cartilage morphology and assess biochemical compositions of the hyaline cartilage to facilitate early diagnostic and treatment for hip osteoarthritis (OA) and FAI.Quantitative analyses can provide useful morphometric data from complex MR data. In the hip joint, the segmentation of bones and cartilages is an essential prerequisite, which must be accurate, reliable and reproducible, for quantitative measurements. However, this is difficult and is traditionally performed using time-and expert-intensive manual or semi-automatic methods.The hypothesis behind this research is that accurate and reproducible quantitative data can be automatically obtained from high-resolution 3D MR images of the hip joint, through the use of advanced image processing techniques. To this end, this research focuses on two specific aims: Aim 1 -to develop and evaluate a fully automated segmentation approach to deliver accurate and reproducible bone and cartilage segmentations from high-resolution 3D MR images of the hip joint and Aim 2 -to automatically extract reliable and reproducible morphometric data based on the segmented subchondral bones and articular cartilages.The development of an automatic segmentation scheme for the bones and cartilages with high precision and reproducibility is firstly needed in order to provide a basis for subsequent quantitative measurements, which deliver reliable morphometric data of the segmented bones and cartilages for the use in early OA and FAI studies. To attain these aims, images were acquired using different MR sequences from a mixed demographic (male or female and young adult to elderly) of participants with a variety of femoral head-neck junction presentations but no apparent hip OA. Different sequence scans were used to image the same participant for the associated reproducibility experiments.Two state-of-the-art methods (multi-atlas-based and active shape model (ASM) based algorithms) i were developed and evaluated for automatic segmentation of proximal femurs and innominate bones from large field of view (FOV) MR images (water-excitation dual echo steady state (DESS) and multi echo data image combination (MEDIC)). The validation results have indicated accurate and robust...

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Section: Declaration By Authormentioning

confidence: 99%

Section: Manual and Semi-automatic Segmentation Of Hip Joint Cartilagementioning

confidence: 99%

Section: Spri Clinical Space and T2 Mapping Imagesmentioning

confidence: 99%

Section: Cartilage Thicknessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated segmentation and quantitative analysis of the hip joint from magnetic resonance images

Xia¹

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

Fernquest

Park

Marčan

et al. 2018

Journal Orthopaedic Research

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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. ß

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Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images

Harischandra

Dharmaratne

Cicuttini

et al. 2020

Communications in Computer and Information Science

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Automated analysis of hip joint cartilage combining MR T2 and three‐dimensional fast‐spin‐echo images

Abstract: The good agreement between the manual and automatic analyses of T2 values indicates the use of structural 3D-SPACE MR images with the proposed method provides a promising approach for automated quantitative T2 assessment of hip joint cartilages.

Cited by 17 publications

References 43 publications

Automated segmentation and quantitative analysis of the hip joint from magnetic resonance images

Automated segmentation and quantitative analysis of the hip joint from magnetic resonance images

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

Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images

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