Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images - data from the Osteoarthritis Initiative (original) (raw)
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Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees
2010
Objective: The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA). Method: The segmentation method was developed then evaluated on 10 baseline MR images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manually choosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, and range constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operation reevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was evaluated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five human readers. Results: The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity index over 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee OA with Osteoarthritis Research Society International (OARSI) joint space narrowing (JSN) scores of 0, one, and two respectively. Conclusion: The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared to manual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thickness, and intensity characteristics at different stages of OA.
IEEE transactions on bio-medical engineering, 2012
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.
Skeletal Radiology, 2009
Purpose Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets. Methods Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm×0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/ lateral tibia (MT/LT), total femur (F) and patella (P). Testretest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%). Results For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me. Conclusion Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA.
An automatic diagnosis method for the knee meniscus tears in MR images
Expert Systems with Applications, 2009
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An automated method to segment the femur for osteoarthritis research
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009
In this paper we develop a fully automated method for the segmentation of the femur in axial MR images and its use in the analysis of imaging biomarkers for osteoarthritis (OA). The proposed method is based on anatomical constraints implemented using morphological operations to extract the femur medulla and a level set evolution to extract the femur cortex. The average agreement of the automated segmentation algorithm with ground truth manual segmentations was 0.94 +/- 0.03 calculated using the Zijdenbos similarity index (ZSI). A pooled variance t-test analysis found significant associations between the KL grade, a clinical measure of OA severity, and both the cross-sectional area (CSA) of the femur medulla (p = 0.02) and the ratio of the femur medulla CSA to the femur cortex CSA (p = 0.04) for women. No significant association between femur measurements and KL grade was found for men.
Skeletal radiology, 2015
To establish a novel method of quantifying meniscal deformation using loaded MRI. More specifically, the goals were to evaluate the (1) accuracy, (2) inter-rater reliability, (3) intra-rater reliability, and (4) scan-rescan reliability. The secondary purpose of this experiment was to evaluate group differences in meniscal deformation in participants with and without radiographic knee OA. Weight-bearing 3-T MRIs of the knee in full extension and 30° of flexion were processed to create 3D models of meniscal deformation. Accuracy was assessed using a custom-designed phantom. Twenty-one participants either with or without signs of OA were evaluated, and another six participants (14 knees, one subject was scanned twice) underwent repeated imaging to assess scan-rescan reproducibility. Intraclass correlation coefficient (ICC), root-mean squared error (RMSE), and root-mean-square percent coefficient-of-variation (RMS%CV) analyses were performed. Exploratory comparisons were made between th...
Novel fast semi-automated software to segment cartilage for knee MR acquisitions
Osteoarthritis and Cartilage, 2007
Objective: Validation 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. Methods: The 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. Results: The repositioning RMSCoV was as follows: VC ¼ 2.0% and ThC ¼ 1.2% (femur), VC ¼ 2.9% and ThC ¼ 1.6% (medial tibial plateau), VC ¼ 5.5% and ThC ¼ 2.4% (lateral tibial plateau), and VC ¼ 4.6% and ThC ¼ 2.3% (patella). RMSCoV values were higher for the inter-reader reproducibility (VC: 2.5e8.6%) (ThC: 1.9e5.2%) and lower for the intra-reader reproducibility (VC: 1.6e2.5%) (ThC: 1.2e1.9%). The method required an average of 75.4 min per knee. Conclusions: We have documented a fast reproducible semi-automated software method to segment articular cartilage on knee MR acquisitions.
Machine Learning Techniques for Quantification of Knee Segmentation from MRI
Hindawi, 2020
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. e advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. is 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. e 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.
Semiautomated digital analysis of knee joint space width using MR images
Skeletal Radiology, 2007
Objective The goal of this study was to (a) develop a semiautomated computer algorithm to measure knee joint space width (JSW) from magnetic resonance (MR) images using standard imaging techniques and (b) evaluate the reproducibility of the algorithm. Design Using a standard clinical imaging protocol, bilateral knee MR images were obtained twice within a 2-week period from 17 asymptomatic research participants. Images were analyzed to determine the variability of the measurements performed by the program compared with the variability of manual measurements. Results Measurement variability of the computer algorithm was considerably smaller than the variability of manual measurements. The average difference between two measurements of the same slice performed with the computer algorithm by the same user was 0.004±0.07 mm for the tibiofemoral joint (TF) and 0.009 ±0.11 mm for the patellofemoral joint (PF) compared with an average of 0.12±0.22 mm TF and 0.13±0.29 mm PF, respectively, for the manual method. Interuser variability of the computer algorithm was also considerably smaller, with an average difference of 0.004±0.1 mm TF and 0.0006±0.1 mm PF compared with 0.38±0.59 mm TF and 0.31±0.66 mm PF obtained using a manual method. The between-day reproducibility was larger but still within acceptable limits at 0.09±0.39 mm TF and 0.09±0.51 mm PF. This technique has proven consistently reproducible on a same slice base, while the reproducibility comparing different acquisitions of the same subject was larger. Longitudinal reproducibility improvement needs to be addressed through acquisition protocol improvements. Conclusion A semiautomated method for measuring knee JSW from MR images has been successfully developed.