Dr. Alexis Wong Profile

Dr. Alexis Wong

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Proceedings Article | 17 March 2008 Paper

Quantitative assessment of multiple sclerosis lesion load using CAD and expert input

Arkadiusz Gertych, Alexis Wong, Alan Sangnil, Brent Liu

Proceedings Volume 6915, 69151U (2008) https://doi.org/10.1117/12.772392

KEYWORDS: Computer aided design, Computer aided diagnosis and therapy, Magnetic resonance imaging, 3D image processing, Brain mapping, Brain, 3D displays, Visualization, Image processing, Image analysis

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Multiple sclerosis (MS) is a frequently encountered neurological disease with a progressive but variable course affecting the central nervous system. Outline-based lesion quantification in the assessment of lesion load (LL) performed on magnetic resonance (MR) images is clinically useful and provides information about the development and change reflecting overall disease burden. Methods of LL assessment that rely on human input are tedious, have higher intra- and inter-observer variability and are more time-consuming than computerized automatic (CAD) techniques. At present it seems that methods based on human lesion identification preceded by non-interactive outlining by CAD are the best LL quantification strategies. We have developed a CAD that automatically quantifies MS lesions, displays 3-D lesion map and appends radiological findings to original images according to current DICOM standard. CAD is also capable to display and track changes and make comparison between patient's separate MRI studies to determine disease progression. The findings are exported to a separate imaging tool for review and final approval by expert. Capturing and standardized archiving of manual contours is also implemented. Similarity coefficients calculated from quantities of LL in collected exams show a good correlation of CAD-derived results vs. those incorporated as expert's reading. Combining the CAD approach with an expert interaction may impact to the diagnostic work-up of MS patients because of improved reproducibility in LL assessment and reduced time for single MR or comparative exams reading. Inclusion of CAD-generated outlines as DICOM-compliant overlays into the image data can serve as a better reference in MS progression tracking.

Proceedings Article | 30 March 2007 Paper

A CAD system for assessment of MRI findings to track the progression of multiple sclerosis

Alexis Wong, Arkadiusz Gertych, Chi-Shing Zee, Bing Guo, Brent Liu

Proceedings Volume 6514, 65142U (2007) https://doi.org/10.1117/12.709932

KEYWORDS: Image segmentation, Brain, Magnetic resonance imaging, CAD systems, Image analysis, Data modeling, Head, Fuzzy logic, 3D image processing, 3D modeling

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Multiple sclerosis (MS) is a progressive neurological disease affecting myelin pathways. MRI has become the medical imaging study of choice both for the diagnosis and for the follow-up and monitoring of multiple sclerosis. The progression of the disease is variable, and requires routine follow-up to document disease exacerbation, improvement, or stability of the characteristic MS lesions or plaques. The difficulties with using MRI as a monitoring tool are the significant quantities of time needed by the radiologist to actually measure the size of the lesions, and the poor reproducibility of these manual measurements. A CAD system for automatic image analysis improves clinical efficiency and standardizes the lesion measurements. Multiple sclerosis is a disease well suited for automated analysis. The segmentation algorithm devised classifies normal and abnormal brain structures and measures the volume of multiple sclerosis lesions using fuzzy c-means clustering with incorporated spatial (sFCM) information. First, an intracranial structures mask in T1 image data is localized and then superimposed in FLAIR image data. Next, MS lesions are identified by sFCM and quantified within a predefined volume. The initial validation process confirms a satisfactory comparison of automatic segmentation to manual outline by a neuroradiologist and the results will be presented.

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