@inproceedings{3eb95f6b25364bd685ac1b1e4bb1ee4b,
title = "Spatio-temporal segmentation of active multiple sclerosis lesions in serial MRI data",
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 spatiotemporal 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.",
author = "Daniel Welti and Guido Gerig and Rad{\"u}, {Ernst Wilhelm} and Ludwig Kappos and Gabor Sz{\'e}kely",
year = "2001",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "438--445",
editor = "Leahy, {Richard M.} and Insana, {Michael F.}",
booktitle = "Information Processing in Medical Imaging - 17th International Conference, IPMI 2001, Proceedings",
note = "17th International Conference on Information Processing in Medical Imaging, IPMI 2001 ; Conference date: 18-06-2001 Through 22-06-2001",
}