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.
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Igual, L., Soliva, J.C., Gimeno, R., Escalera, S., Vilarroya, O., Radeva, P. (2012). Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention-Deficit/Hyperactivity Disorder. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_27
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DOI: https://doi.org/10.1007/978-3-642-31298-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31297-7
Online ISBN: 978-3-642-31298-4
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