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Proceedings Paper

An automatic quantification system for MS lesions with integrated DICOM structured reporting (DICOM-SR) for implementation within a clinical environment
Author(s): Colin Jacobs; Kevin Ma; Paymann Moin; Brent Liu
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Paper Abstract

Multiple Sclerosis (MS) is a common neurological disease affecting the central nervous system characterized by pathologic changes including demyelination and axonal injury. MR imaging has become the most important tool to evaluate the disease progression of MS which is characterized by the occurrence of white matter lesions. Currently, radiologists evaluate and assess the multiple sclerosis lesions manually by estimating the lesion volume and amount of lesions. This process is extremely time-consuming and sensitive to intra- and inter-observer variability. Therefore, there is a need for automatic segmentation of the MS lesions followed by lesion quantification. We have developed a fully automatic segmentation algorithm to identify the MS lesions. The segmentation algorithm is accelerated by parallel computing using Graphics Processing Units (GPU) for practical implementation into a clinical environment. Subsequently, characterized quantification of the lesions is performed. The quantification results, which include lesion volume and amount of lesions, are stored in a structured report together with the lesion location in the brain to establish a standardized representation of the disease progression of the patient. The development of this structured report in collaboration with radiologists aims to facilitate outcome analysis and treatment assessment of the disease and will be standardized based on DICOM-SR. The results can be distributed to other DICOM-compliant clinical systems that support DICOM-SR such as PACS. In addition, the implementation of a fully automatic segmentation and quantification system together with a method for storing, distributing, and visualizing key imaging and informatics data in DICOM-SR for MS lesions improves the clinical workflow of radiologists and visualizations of the lesion segmentations and will provide 3-D insight into the distribution of lesions in the brain.

Paper Details

Date Published: 11 March 2010
PDF: 8 pages
Proc. SPIE 7628, Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 76280K (11 March 2010); doi: 10.1117/12.844072
Show Author Affiliations
Colin Jacobs, Eindhoven Univ. of Technology (Netherlands)
Kevin Ma, Image Processing and Informatics Lab., The Univ. of Southern California (United States)
Paymann Moin, Image Processing and Informatics Lab., The Univ. of Southern California (United States)
Brent Liu, Image Processing and Informatics Lab., The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 7628:
Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications
Brent J. Liu; William W. Boonn, Editor(s)

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