A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation

• Published in: IEEE transactions on medical imaging Vol. 34; no. 6; pp. 1349 - 1361
• Main Authors: Tomas-Fernandez, Xavier, Warfield, Simon K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2015
• Subjects: Algorithms; Brain - pathology; Brain modeling; Diseases; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; multiple sclerosis (MS); Multiple Sclerosis - pathology; segmentation; Sensitivity and Specificity; Sociology; Statistics; tissue classification
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2015.2393853

White matter (WM) lesions are thought to play an important role in multiple sclerosis (MS) disease burden. Recent work in the automated segmentation of white matter lesions from magnetic resonance imaging has utilized a model in which lesions are outliers in the distribution of tissue signal intensities across the entire brain of each patient. However, the sensitivity and specificity of lesion detection and segmentation with these approaches have been inadequate. In our analysis, we determined this is due to the substantial overlap between the whole brain signal intensity distribution of lesions and normal tissue. Inspired by the ability of experts to detect lesions based on their local signal intensity characteristics, we propose a new algorithm that achieves lesion and brain tissue segmentation through simultaneous estimation of a spatially global within-the-subject intensity distribution and a spatially local intensity distribution derived from a healthy reference population. We demonstrate that MS lesions can be segmented as outliers from this intensity model of population and subject. We carried out extensive experiments with both synthetic and clinical data, and compared the performance of our new algorithm to those of state-of-the art techniques. We found this new approach leads to a substantial improvement in the sensitivity and specificity of lesion detection and segmentation.