02105nas a2200169   4500008004100000245009700041210006900138260004200207300001600249520142000265100002201685700002801707700001401735700002401749700002301773856013901796       2000                        eng d00aMultiprotocol MR image segmentation in multiple sclerosis: experience with over 1000 studies0 aMultiprotocol MR image segmentation in multiple sclerosis experi  aBellingham; WashingtonbSPIEc2000///  a1017 - 10273 aMultiple Sclerosis (MS) is an acquired disease of the centralnervous system. Subjective cognitive and ambulatory test scores 
on a scale called EDSS are currently utilized to assess the 
disease severity. Various MRI protocols are being investigated 
to study the disease based on how it manifests itself in the 
images. In an attempt to eventually replace EDSS by an objective 
measure to assess the natural course of the disease and its 
response to therapy, we have developed image segmentation 
methods based on fuzzy connectedness to quantify various objects 
in multiprotocol MRI. These include the macroscopic objects such 
as lesions, the gray matter (GM), white matter (WM), 
cerebrospinal fluid (CSF), and brain parenchyma as well as the 
microscopic aspects of the diseased WM. Over 1000 studies have 
been processed to date. By far the strongest correlations with 
the clinical measures were demonstrated by the Magnetization 
Transfer Ratio (MTR) histogram parameters obtained for the 
various segmented tissue regions emphasizing the importance of 
considering the microscopic/diffused nature of the disease in 
the individual tissue regions. Brain parenchymal volume also 
demonstrated a strong correlation with the clinical measures 
indicating that brain atrophy is an important indicator of the 
disease. Fuzzy connectedness is a viable segmentation method for 
studying MS.
1 aUdupa, Jayaram, K1 aNyúl, László, Gábor1 aGe, Yiyue1 aGrossman, Robert, J1 aHanson, Kenneth, M  uhttps://home.inf.u-szeged.hu/publication/multiprotocol-mr-image-segmentation-in-multiple-sclerosis-experience-with-over-1000-studies-0