scholarly journals A robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation

2008 ◽  
Author(s):  
Daniel Garcia-Lorenzo ◽  
Sylvain Prima ◽  
Sean P. Morrissey ◽  
Christian Barillot
Keyword(s):  
Multiple Sclerosis ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
User Interaction ◽  
Intensity Inhomogeneity ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Skull Stripping ◽  
Fully Automatic

A fully automatic workflow for Multiple Sclerosis (MS) lesion segmentation is described. Fully automatic means that no user interaction is performed in any of the steps and that all parameters are fixed for all the images processed in beforehand. Our workflow is composed of three steps: an intensity inhomogeneity (IIH) correction, skull-stripping and MS lesions segmentation. A validation comparing our results with two experts is done on MS MRI datasets of 24 MS patients from two different sites.

scholarly journals Multiple Sclerosis Lesion Segmentation Using Statistical and Topological Atlases

2008 ◽  
Author(s):  
Navid Shiee ◽  
Pierre-Louis Bazin ◽  
Dzung L. Pham
Keyword(s):  
Multiple Sclerosis ◽  
Magnetic Resonance Images ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Brain Images ◽  
Analysis Techniques ◽  
Segmentation Methods ◽  
Fully Automatic ◽  
The Brain ◽  
Statistical Atlas

This paper presents a new fully automatic method for segmentation of brain images that possess multiple sclerosis (MS) lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing with cortical unfolding or diffeomorphic shape analysis techniques. Validation on data from two studies demonstrates that the method has an accuracy comparable with other MS lesion segmentation methods, while simultaneously segmenting the whole brain.

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials

Radiology ◽  
2021 ◽  
Author(s):  
Anitha Priya Krishnan ◽  
Zhuang Song ◽  
David Clayton ◽  
Laura Gaetano ◽  
Xiaoming Jia ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

Level set evolution with intensity prior knowledge for multiple sclerosis lesion segmentation

2019 ◽  
Vol 5 (1) ◽  
pp. 37
Author(s):  
Guodong Zhang ◽  
Zhaoxuan Gong ◽  
Wei Guo ◽  
Zhenyu Zhu ◽  
Jia Guo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Prior Knowledge ◽  
Level Set ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Level Set Evolution

A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRI

2019 ◽  
Vol 13 (5) ◽  
pp. 1019-1027
Author(s):  
Jingjing Wang ◽  
Changjun Hu ◽  
Huaqiang Xu ◽  
Yan Leng ◽  
Liren Zhang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation
Sign in / Sign up

Export Citation Format

Share Document