Automatic internal segmentation of caudate nucleus for diagnosis of attention-deficit/hyperactivity disorder

Authors:
Laura Igual;Joan Carles Soliva;Roger Gimeno;Sergio Escalera;Oscar Vilarroya;Petia Radeva
Affiliations:
Department of Applied Mathematics and Analysis, Universitat of Barcelona, Spain, Computer Vision Center of Barcelona, Spain;Unitat de Recerca en Neurociència Cognitiva, Department of Psychiatry, Universitat Autònoma de Barcelona, Spain, Fundació IMIM, Spain;Computer Vision Center of Barcelona, Spain;Department of Applied Mathematics and Analysis, Universitat of Barcelona, Spain, Computer Vision Center of Barcelona, Spain;Unitat de Recerca en Neurociència Cognitiva, Department of Psychiatry, Universitat Autònoma de Barcelona, Spain, Fundació IMIM, Spain;Department of Applied Mathematics and Analysis, Universitat of Barcelona, Spain, Computer Vision Center of Barcelona, Spain
Venue:
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Year:
2012

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Abstract

Studies on volumetric brain Magnetic Resonance Imaging (MRI) showed neuroanatomical abnormalities in pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). In particular, the diminished right caudate volume is one of the most replicated findings among ADHD samples in morphometric MRI studies. In this paper, we propose a fully-automatic method for internal caudate nucleus segmentation based on machine learning. Moreover, the ratio between right caudate body volume and the bilateral caudate body volume is applied in a ADHD diagnostic test. We separately validate the automatic internal segmentation of caudate in head and body structures and the diagnostic test using real data from ADHD and control subjects. As a result, we show accurate internal caudate segmentation and similar performance among the proposed automatic diagnostic test and the manual annotation.