Work

Automatic atlas-based three-label cartilage segmentation from MR knee images

Year: 2012

Type: conference-paper

Abstract: This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilistic k nearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 78.2% and 82.6% respectively).

Source: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA)

DOI: 10.1109/mmbia.2012.6164757

Authors: Liang Shan, Cecil Charles, Marc Niethammer

Institutions: University of North Carolina at Chapel Hill, Duke University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Pfizer, National Institute of Arthritis and Musculoskeletal and Skin Diseases