Knee cartilage segmentation and thickness computation from ultrasound images

Abstract

Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.

Developing a toolkit for the assessment and monitoring of musculoskeletal ageing

“…While knee femoral cartilage can be imaged, tibial and most hand joints cannot, especially in those whose flexion is limited by age or pathology. There are no published population cohort data on femoral cartilage thickness, but recent case-control studies [ 111 ] and methodological work [ 112 ] how that undertaking such measurements in cohort studies is feasible.…”

Three-dimensional ultrasound for knee osteophyte depiction: a comparative study to computed tomography

“…3D US could represent a low-cost, dynamic and minimally invasive modality for capturing osteophytes, which can be incorporated into routine diagnostic evaluations and pre-operative planning. Automated [19,21,[32][33][34][35][36] have been published to overcome the limitations of registration and manual segmentation which are not practical clinically. Advances in automatic segmentation algorithms include the ability to distinguish tissue features, such as cartilage thickness and bone contours, from noisy US images of the knee [35] and other anatomical regions such as the spine [36], hip [32] and pelvis [19].…”

Artificial intelligence in musculoskeletal ultrasound imaging

“…[66] used a histogram equalization method of multipurpose beta optimized recursive bi-histogram equalizations to achieve the optimum values of contrast and brightness and to preserve details in the enhancement process of US knee cartilage images. To obtain the true thickness between soft tissue cartilage interface and cartilagebone interface, quantitative segmentation of the monotonous hypoechoic band was obtained using a locally statistical level set method [67]. Similarly, automated knee-bone surface localization was used to obtain seed points for semi-automatic segmentation methods (random walker, watershed, and graph-cut algorithms), and distance maps are applied to obtain mean cartilage thickness [68].…”