Spatio-temporal Segmentation of Active Multiple Sclerosis Lesions in Serial MRI Data

Authors:
Daniel Welti;Guido Gerig;Ernst-Wilhelm Radü;Ludwig Kappos;Gábor Székely
Affiliations:
-;-;-;-;-
Venue:
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Year:
2001

Citing 4
Cited 5

Invariant geometric evolutions of surfaces and volumetric smoothing

SIAM Journal on Applied Mathematics
Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis

IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Exploring the Discrimination Power of the Time Domain for Segmentation and Characterization of Lesions in Serial MR Data

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Compensation of Spatial Inhomogeneity in MRI Based on a Parametric Bias Estimate

VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing

Statistical Analysis of Longitudinal MRI Data: Applications for Detection of Disease Activity in MS

MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Segmentation of brain tumors in 4D MR images using the hidden Markov model

Computer Methods and Programs in Biomedicine
Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
STREM: a robust multidimensional parametric method to segment MS lesions in MRI

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Extracting evolving pathologies via spectral clustering

IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new approach for the automatic segmentation and characterization of active MS lesions in 4D data of multiple sequences. Traditional segmentation of 4D data applies individual 3D spatial segmentation to each image data set, thus not making use of correlation over time. More recently, a time series analysis has been applied to 4D data to reveal active lesions [3]. However, misregistration at tissue borders led to false positive lesion voxels. Lesion development is a complex spatio-temporal process, consequently methods concentrating exclusively on the spatial or temporal aspects of it cannot be expected to provide optimal results. Active MS lesions were extracted from the 4D data in order to quantify MR-based spatio-temporal changes in the brain. A spatio-temporal lesion model generated by principal component analysis allowed robust identification of active MS lesions overcoming the drawbacks of traditional purely spatial or purely temporal segmentation methods.