02385nas a2200181   4500008004100000020001400041245009800055210006900153260001200222300001600234490000600250520172700256100002201983700002802005700001402033700002402047856013202071       2001                        eng d  a1076-633200aMultiprotocol MR image segmentation in multiple sclerosis: Experience with over 1,000 studies0 aMultiprotocol MR image segmentation in multiple sclerosis Experi  c2001///  a1116 - 11260 v83 aRATIONALE AND OBJECTIVES: Multiple sclerosis (MS) is an acquireddisease of the central nervous system. Several clinical measures 
are commonly used to express the severity of the disease, 
including the Expanded Disability Status Scale and the 
ambulation index. These measures are subjective and may be 
difficult to reproduce. The aim of this research is to 
investigate the possibility of developing more objective 
measures derived from MR imaging. MATERIALS AND METHODS: Various 
magnetic resonance (MR) imaging protocols are being investigated 
for the study of MS. Seeking to replace the Expanded Disability 
Status Scale and ambulation index with an objective means to 
assess the natural course of the disease and its response to 
therapy, the authors have developed multiprotocol MR image 
segmentation methods based on fuzzy connectedness to quantify 
both macrosopic features of the disease (lesions, gray matter, 
white matter, cerebrospinal fluid, and brain parenchyma) and the 
microscopic appearance of diseased white matter. Over 1,000 
studies have been processed to date. RESULTS: By far the 
strongest correlations with the clinical measures were 
demonstrated by the magnetization transfer ratio histogram 
parameters obtained for the various segmented tissue regions. 
These findings emphasize the importance of considering the 
microscopic and diffuse nature of the disease in the individual 
tissue regions. Brain parenchymal volume also demonstrated a 
strong correlation with clinical measures, which suggests that 
brain atrophy is an important disease indicator. CONCLUSION: 
Fuzzy connectedness is a viable, highly reproducible 
segmentation method for studying MS.
1 aUdupa, Jayaram, K1 aNyúl, László, Gábor1 aGe, Yiyue1 aGrossman, Robert, J  uhttps://www.inf.szte.hu/publication/multiprotocol-mr-image-segmentation-in-multiple-sclerosis-experience-with-over-1000-studies