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

Abstract

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

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

“…Of note, previous studies presenting different techniques for automated cartilage segmentation did not describe the osseous acetabular morphology in their patient population [20,39,49], excluded hips with pincer FAI or acetabular dysplasia [14], or only included asymptomatic volunteers [10]. Acetabular morphology reportedly affects the size and shape of the lunate surface in patients eligible for joint-preserving hip surgery [53].…”

“…To ensure a wide applicability of our segmentation approach, we chose a representative patient cohort that included hips with acetabular dysplasia, normal acetabular coverage, acetabular retroversion, and acetabular overcoverage (Table 1). Furthermore, our approach yielded the advantage of providing a more comprehensive assessment of hip cartilage, including thickness, surface area, and volume while previous studies only focused on validating different approaches for extracting biochemical cartilage composition using dGEMRIC [14,20,49], T2 [10,39], and T1rho [39]. In addition, it is important to mention that the methodology for automated cartilage segmentation can be applied to any other high-resolution 3-D cartilage MR technique such as T2* mapping [22].…”

“…In current practice, most of the 3-D data are not used, in favor of a more timeefficient analysis [49]. To overcome this limitation, researchers have developed techniques for a more automated [10,14,20,39,49] computer-assisted 3-D assessment of biochemical cartilage damage. These techniques are based on the manual labelling or segmentation of cartilage to generate a 3-D cartilage model.…”

“…These data then are used to train an algorithm to automatically generate 3-D models of hip cartilage. However, some of the available approaches require multiple manual steps [14,20,49] and are based on asymptomatic volunteers only [10], while others used MRI sequences, which are difficult to implement in clinical practice because of relatively long image-acquisition times [14,20,39]. In addition to assessing cartilage quality, the use of 3-D MRI offers the possibility to simultaneously provide information on cartilage thickness, cartilage surface area, and cartilage volume, thus allowing for a potentially improved comprehensive hip assessment.…”

Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI

“…In this paper, we sought to compare manual segmentation to semi-automated segmentation on several variables, both quantitative and qualitative, for segmentation of VS. It is widely published that semi-automated segmentation may reduce the time taken to perform segmentation [9,23,30]. We showed that semi-automated segmentation is significantly faster and has comparable performance when compared with manual segmentation for volumetric analysis of VS.…”