Machine Learning Techniques for Quantification of Knee Segmentation from MRI

© 2020 Sujeet More et al. Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed

Novel fast semi-automated software to segment cartilage for knee MR acquisitions

AbstractObjectiveValidation of a new fast software technique to segment the cartilage on knee magnetic resonance (MR) acquisitions. Large studies of knee osteoarthritis (OA) will require fast and reproducible methods to quantify cartilage changes for knee MR data. In this report we document and measure the reproducibility and reader time of a software-based technique to quantify the volume and thickness of articular cartilage on knee MR images.MethodsThe software was tested on a set of duplicate sagittal three-dimensional (3D) dual echo steady state (DESS) acquisitions from 15 (8 OA, 7 normal) subjects. The repositioning, inter-reader, and intra-reader reproducibility of the cartilage volume (VC) and thickness (ThC) were measured independently as well as the reader time for each cartilage plate. The root-mean square coefficient of variation (RMSCoV) was used as metric to quantify the reproducibility of VC and mean ThC.ResultsThe repositioning RMSCoV was as follows: VC=2.0% and ThC=1.2% (femur), VC=2.9% and ThC=1.6% (medial tibial plateau), VC=5.5% and ThC=2.4% (lateral tibial plateau), and VC=4.6% and ThC=2.3% (patella). RMSCoV values were higher for the inter-reader reproducibility (VC: 2.5–8.6%) (ThC: 1.9–5.2%) and lower for the intra-reader reproducibility (VC: 1.6–2.5%) (ThC: 1.2–1.9%). The method required an average of 75.4min per knee.ConclusionsWe have documented a fast reproducible semi-automated software method to segment articular cartilage on knee MR acquisitions

Autonomous Direct 3D Segmentation of Articular Knee Cartilage

The aim of the work presented here, is to speed up the entire evaluation process of articular knee cartilage and the associated medication developments for Osteoarthritis. To enable this, the development of an automated direct 3D segmentation is described that incorporates non-linear diffusion for efficient image denoising. Cartilage specific magnetic resonance imaging is used, which allows acquiring the entire cartilage volume as one 3D image. The segmentation itself is based on level sets for their accuracy, stability and topological flexibility. By using this kind of segmentation, it is hoped to improve the time efficiency and accuracy for quantitative and qualitative integrity evaluation of cartilage and to enable an earlier diagnosis and treatment of Osteoarthritis

Quantitative Cartilage Imaging in Knee Osteoarthritis

Quantitative measures of cartilage morphology (i.e., thickness) represent potentially powerful surrogate endpoints in osteoarthritis (OA). These can be used to identify risk factors of structural disease progression and can facilitate the clinical efficacy testing of structure modifying drugs in OA. This paper focuses on quantitative imaging of articular cartilage morphology in the knee, and will specifically deal with different cartilage morphology outcome variables and regions of interest, the relative performance and relationship between cartilage morphology measures, reference values for MRI-based knee cartilage morphometry, imaging protocols for measurement of cartilage morphology (including those used in the Osteoarthritis Initiative), sensitivity to change observed in knee OA, spatial patterns of cartilage loss as derived by subregional analysis, comparison of MRI changes with radiographic changes, risk factors of MRI-based cartilage loss in knee OA, the correlation of MRI-based cartilage loss with clinical outcomes, treatment response in knee OA, and future directions of the field