Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

• Published in: International journal for computer assisted radiology and surgery Vol. 15; no. 3; pp. 403 - 413
• Main Authors: Thaha, Rafeek, Jogi, Sandeep P., Rajan, Sriram, Mahajan, Vidur, Venugopal, Vasantha K., Mehndiratta, Amit, Singh, Anup
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2020; Springer Nature B.V
• Subjects: Abnormalities; Adult; Algorithms; Biomedical materials; Cartilage; Cartilage, Articular - diagnostic imaging; Coefficient of variation; Computer Imaging; Computer Science; Curve fitting; Female; Femur; Femur - diagnostic imaging; Health Informatics; Humans; Image acquisition; Image segmentation; Imaging; Knee; Knee Joint - diagnostic imaging; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Original Article; Osteoarthritis, Knee - diagnostic imaging; Pattern Recognition and Graphics; Proton density (concentration); Radiology; Retrospective Studies; Search algorithms; Search methods; Surgery; Tibia - diagnostic imaging; Vision; Young Adult; Subchondral bone abnormality; Articular cartilage; Segmentation; MRI; Osteoarthritis
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-020-02116-z

Purpose The quantitative analysis of weight-bearing articular cartilage superficial to subchondral abnormality is important in osteoarthritis (OA) progression studies. The current study aimed to address the challenges of a semi-automatic segmentation of tibiofemoral cartilage in MR images of OA patient with and without subchondral bone abnormalities (SBA). Methods In this study, knee MRI data [fat-suppressed proton density-weighted, multi-echo T2-weighted (CartiGram) images] of 29 OA patients, acquired at 3.0T MR scanner, were retrospectively collected. Out of 29 data, 9 had SBA in femur bone. Initially, a semi-automatic femur cartilage segmentation based on radial intensity search approach by Akhtar et al. was implemented in-house. This algorithm was considered as the radial-search method for further comparison. In this current study, the reported radial-search (RS)-based semi-automatic cartilage segmentation method was modified using thresholding, connected component labelling, convex-hull operation and spline-based curve fitting for the improved segmentation of tibiofemoral cartilage. Cartilage was manually segmented by two experienced radiologists, and inter-reader variability was estimated using coefficient of variation (CV). The segmentation results were validated using dice coefficient (DC), Jaccard coefficient (JC) and sensitivity index measurements. Results DC values for segmented femur cartilage in patients with SBA were 64.6 ± 7.8% and 81.4 ± 2.8% using reported RS method and modified radial-search method, respectively. DC values for segmented femur cartilage in patients without SBA were 82.5 ± 4.5% and 84.8 ± 2.0% using RS method and modified radial method, respectively. Similarly, DC values for tibial cartilage in all OA patients were 80.4 ± 1.6% and 81.9 ± 2.4% using RS method and modified radial method, respectively. Similar segmentation results were also obtained from the T2-weighted images. Inter-reader variability result based on CV in femur cartilage was 3.40 ± 2.12% (without SBA) and 4.18 ± 3.18% (with SBA). Conclusion In the current study, a semi-automated segmentation of tibiofemoral cartilage was presented. Modified radial-search approach can successfully segment tibiofemoral cartilage, and the results were tested and validated on knee MRI data of OA patients with and without SBA.