Paper Info
Title
Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution.
Abstract
The automatic analysis of subtle changes between MRI scans is an important tool for assessing disease evolution over time. Manual labeling of evolutions in 3D data sets is tedious and error prone. Automatic change detection, however, remains a challenging image processing problem. A variety of MRI artifacts introduce a wide range of unrepresentative changes between images, making standard change detection methods unreliable. In this study we describe an automatic image processing system that addresses these issues. Registration errors and undesired anatomical deformations are compensated using a versatile multiresolution deformable image matching method that preserves significant changes at a given scale. A nonlinear intensity normalization method is associated with statistical hypothesis test methods to provide reliable change detection. Multimodal data is optionally exploited to reduce the false detection rate. The performance of the system was evaluated on a large database of 3D multimodal, MR images of patients suffering from relapsing remitting multiple sclerosis (MS). The method was assessed using receiver operating characteristics (ROC) analysis, and validated in a protocol involving two neurologists. The automatic system outperforms the human expert, detecting many lesion evolutions that are missed by the expert, including small, subtle changes.
Year
DOI
Venue
2003
10.1016/S1053-8119(03)00406-3
NeuroImage
Keywords
DocType
Volume
Change detection,Deformable matching,Statistical tests,Serial MRI,Multiple sclerosis
Journal
20
Issue
ISSN
Citations 
2
1053-8119
57
PageRank 
References 
Authors
2.94
15
6
Name
Order
Citations
PageRank
Marcel Bosc1623.48
Fabrice Heitz240159.55
Jean-Paul Armspach322126.60
Izzie Namer46811.51
Daniel Gounot5583.40
Lucien Rumbach6765.87