Detection of Gad-Enhancing Lesions in Multiple Sclerosis Using Conditional Random Fields
Identification of Gad-enhancing lesions is of great interest in Multiple Sclerosis (MS) disease since they are associated with disease activity. Current techniques for detecting Gad-enhancing lesions use a contrast agent (Gadolinium) which is administered intravenously to highlight Gad-enhancing lesions. However, the contrast agent not only highlights these lesions, but also causes other tissues (e.g. blood vessels) or noise in the Magnetic Resonance Image (MRI) to appear hyperintense. Discrimination of enhanced lesions from other enhanced structures is particularly challenging as these lesions are typically small and can be found in close proximity to vessels.We present a new approach to address the segmentation of Gad-enhancing MS lesions using Conditional Random Fields (CRF). CRF performs the classification by simultaneously incorporating the spatial dependencies of data and labels. The performance of the CRF classifier on 20 clinical data sets shows promising results in successfully capturing all Gad-enhancing lesions. Furthermore, the quantitative results of the CRF classifier indicate a reduction in the False Positive (FP) rate by an average factor of 5.8 when comparing to Linear Discriminant Analysis (LDA) and 1.6 comparing to a Markov Random Field (MRF) classifier.
Zahra Karimaghaloo Mohak Shah Simon J.Francis Douglas L.Arnold D.Louis Collins and Tal Arbel
Centre for Intelligent Machines, McGill University, Canada Centre for Intelligent Machines, McGill University, Canada NeuroRx Research, Montreal, Canada Montreal Neurological Institute, McGill University, Canada NeuroRx Research, Montreal, Canada
国际会议
北京
英文
41–48
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）