Atlas-Based Segmentation of Degenerated Lumbar Intervertebral Discs From MR Images of the Spine

• Published in: IEEE transactions on biomedical engineering Vol. 56; no. 9; pp. 2225 - 2231
• Main Authors: Michopoulou, Sofia K., Costaridou, Lena, Panagiotopoulos, Elias, Speller, Robert, Panayiotakis, George, Todd-Pokropek, Andrew
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Back; Bayes Theorem; Biomedical imaging; Clinical medicine; Cluster Analysis; Computer aided diagnosis; Diagnosis, Computer-Assisted; Disc degeneration; Fuzzy Logic; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Intervertebral Disc - pathology; intervertebral discs; Lumbar Vertebrae - anatomy & histology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnostic imaging; MRI; Pain; Physics; Reproducibility of Results; ROC Curve; Spine; Spine - anatomy & histology; Surgery
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2009.2019765

Intervertebral disc degeneration is an age-associated condition related to chronic back pain, while its consequences are responsible for over 90% of spine surgical procedures. In clinical practice, MRI is the modality of reference for diagnosing disc degeneration. In this study, we worked toward 2-D semiautomatic segmentation of both normal and degenerated lumbar intervertebral discs from T2-weighted midsagittal MR images of the spine. This task is challenged by partial volume effects and overlapping gray-level values between neighboring tissue classes. To overcome these problems three variations of atlas-based segmentation using a probabilistic atlas of the intervertebral disc were developed and their accuracies were quantitatively evaluated against manually segmented data. The best overall performance, when considering the tradeoff between segmentation accuracy and time efficiency, was accomplished by the atlas-robust-fuzzy c -means approach, which combines prior anatomical knowledge by means of a rigidly registered probabilistic disc atlas with fuzzy clustering techniques incorporating smoothness constraints. The dice similarity indexes of this method were 91.6% for normal and 87.2% for degenerated discs. Research in progress utilizes the proposed approach as part of a computer-aided diagnosis system for quantification and characterization of disc degeneration severity. Moreover, this approach could be exploited in computer-assisted spine surgery.