A Longitudinal Model for Variations Detection in White Matter Fiber-Bundles

被引：0

作者：

Stamile, Claudio ^{[1
]}

Kocevar, Gabriel ^{[1
]}

Cotton, Francois ^{[1
,2
]}

Hannoun, Salem ^{[1
]}

Durand-Dubief, Francoise ^{[1
,3
]}

Frindel, Carole ^{[1
]}

Rousseau, David ^{[1
]}

Sappey-Marinier, Dominique ^{[1
,4
]}

机构：

[1] Univ Lyon 1, CNRS, UMR 5220, INSERM,U1044,CREATIS,INSA Lyon, F-69621 Villeurbanne, France

[2] Hosp Civils Lyon, Ctr Hosp Lyon Sud, Serv Radiol, Pierre Benite, France

[3] Hosp Civils Lyon, Hop Neurol, Serv Neurol A, Bron, France

[4] Univ Lyon, CERMEP Imagerie Vivant, Bron, France

来源：

2015 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP 2015) | 2015年

关键词：

Genetic Algorithm; White Matter; Multiple Sclerosis; Tractography; Longitudinal Analysis; TRACTOGRAPHY; BRAIN;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Processing of longitudinal diffusion tensor imaging (DTI) data is a crucial challenge to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white matter (WM) fiber bundles are variably altered by inflammatory events. In this work, we propose a new fully automated method to detect significant longitudinal changes in diffusivity metrics along WM fiber-bundles. This method consists in two steps: i) preprocessing of longitudinal diffusion acquisitions and WM fiber-bundles extraction, ii) application of a genetic algorithm (GA) to detect "pathological" changes. This method was applied first, on simulated longitudinal variations, and second, on MS patients longitudinal data. High level of precision, recall and F-Measure were obtained for the detection of small longitudinal changes along the WM fiber-bundles.

引用

页码：57 / 60

页数：4

共 17 条

[1]

[Anonymous], 2024, P INT SCI CONFERENCE

[2]

[Anonymous], 2001, MultiObjective Optimization Using Evolutionary Algorithms

[3]

De Jong K.A., 1990, P INT WORKSHOP PARAL, P38

[4] A fast and elitist multiobjective genetic algorithm: NSGA-II [J].