Unsupervised Segmentation and Quantification of Anatomical Knee Features: Data From the Osteoarthritis Initiative

Abstract

This paper presents a fully automated method for segmenting articular knee cartilage and bone from in vivo 3-D dual echo steady state images. The magnetic resonance imaging (MRI) datasets were obtained from the Osteoarthritis Initiative (OAI) pilot study and include longitudinal images from controls and subjects with knee osteoarthritis (OA) scanned twice at each visit (baseline, 24 month). Initially, human experts segmented six MRI series. Five of the six resultant sets served as reference atlases for a multiatlas segmentation algorithm. The methodology created precise knee segmentations that were used to extract articular cartilage volume, surface area, and thickness as well as subchondral bone plate curvature. Comparison to manual segmentation showed Dice similarity coefficient (DSC) of 0.88 and 0.84 for the femoral and tibial cartilage. In OA subjects, thickness measurements showed test-retest precision ranging from 0.014 mm (0.6%) at the femur to 0.038 mm (1.6%) at the femoral trochlea. In the same population, the curvature test-retest precision ranged from 0.0005 mm(-1) (3.6%) at the femur to 0.0026 mm(-1) (11.7%) at the medial tibia. Thickness longitudinal changes showed OA Pearson correlation coefficient of 0.94 for the femur. In conclusion, the fully automated segmentation methodology produces reproducible cartilage volume, thickness, and shape measurements valuable for the study of OA progression.

Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

“…18,36,[38][39][40][41][42] Other methods achieve a similar coarse-to-fine effect by applying atlas-based registration before either nonrigid registration 21,40,43 and/or classifier-based segmentation. 10,22,43 In general, it appears that integration of global information and local features is essential for solving the challenging problem of segmentation of cartilage on the background of multiple other tissue types. This explicitly or implicitly allows a zoom to several, simpler, local segmentation tasks such as cartilage versus subchondral bone, cartilage versus meniscus, cartilage versus cartilage, and cartilage versus synovial fluid.…”

Fully automated segmentation of cartilage from the MR images of knee using a multi‐atlas and local structural analysis method

“…Rather, the performance of their segmentation methods was assessed by using a leaveone-out method. 4,7 In our study, with a large number of training cases, we were able to use 40 cases as the cross validation set. We believe this approach strengthens and improves the rigor for the development and performance test of our proposed scheme.…”

“…However, several groups recently proposed "fully automated methods with no user interaction" for segmenting cartilage and bone from MR images. [4][5][6][7][8] Folkesson et al proposed k-nearest neighbor framework to perform tissue classification by selecting features such as voxel position, raw and Gaussian smoothed intensities, and intensity derivatives. 8 Fripp et al presented a segmentation scheme with three-dimensional active shape models.…”

“…12,13 This limitation may be overcome, though, by using a multi-atlas segmentation approach, 14,15 derived from separately registering multiple atlases to the target and resolving voxel-wise label conflicts between the registered atlases through label fusion. While Tamez-Pena et al reported promising results using the multi-atlas based cartilage segmentation algorithm, 4 they incorporated only a simple labelfusion approach and did not use any further correction methods to address abnormalities, such as osteophytes that form along joint margins. In the label fusion, it is important to find the corresponding position in multiple atlases, which in previous approaches has been accomplished by using the signal intensity of voxels.…”

A fully automated system for quantification of knee bone marrow lesions using MRI and the osteoarthritis initiative cohort

“…In the last decade, scoring of BMLs in MRI systematically appears in the various osteoarthritis knee evaluation systems contributing to the final grade of an overall knee condition [9][10][11][12][13] commonly used in longitudinal studies. In parallel, the combination of MRI and image processing techniques has allowed several semiautomatic and automatic systems to be developed for articular tissue segmentation including bone [14][15][16][17][18] , cartilage [19][20][21][22][23] , menisci [24] , and synovitis [25] for quantitative and semi-quantitative evaluation. However, very few technologies were developed looking at automatically assessing the volume of BMLs.…”