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Optimized cortical subdivision for classification of Alzheimer's disease with cortical thickness

Optimized cortical subdivision for classification of Alzheimer's disease with cortical thickness

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RICHTER, Mirco, Dorit MERHOF, 2013. Optimized cortical subdivision for classification of Alzheimer's disease with cortical thickness. In: MEINZER, Hans-Peter, ed., Thomas Martin DESERNO, ed., Heinz HANDELS, ed., Thomas TOLXDORFF, ed.. Bildverarbeitung für die Medizin 2013. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 33-38. ISBN 978-3-642-36479-2. Available under: doi: 10.1007/978-3-642-36480-8_8

@inproceedings{Richter2013-02-20Optim-22405, title={Optimized cortical subdivision for classification of Alzheimer's disease with cortical thickness}, year={2013}, doi={10.1007/978-3-642-36480-8_8}, isbn={978-3-642-36479-2}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Informatik aktuell}, booktitle={Bildverarbeitung für die Medizin 2013}, pages={33--38}, editor={Meinzer, Hans-Peter and Deserno, Thomas Martin and Handels, Heinz and Tolxdorff, Thomas}, author={Richter, Mirco and Merhof, Dorit} }

eng In several studies, brain atrophy measured by cortical thickness has shown to be a meaningful biomarker for Alzheimer’s disease. In this research field, the level of granularity at which values are compared is an important aspect. Vertex- and voxel-based approaches can detect atrophy at a very fine scale, but are susceptible to noise from misregistrations and inter-subject differences in the population. Regional approaches are more robust to these kinds of noise, but cannot detect variances at a local scale. In this work, an optimized classifier is presented for a parcellation scheme that provides a trade-off between both paradigms by increasing the granularity of a regional approach. For this purpose, atlas regions are subdivided into gyral and sulcal parts at different height levels. Using two-stage feature selection, optimal gyral and sulcal subregions are determined for the final classification with sparse logistic regression. The robustness was assessed on clinical data by 10- fold cross-validation and by testing the prediction accuracy for unseen individuals. In every aspect, superior classification performance was observed as compared to the original parcellation scheme which can be<br />explained by the increased locality of cortical thickness measures and<br />the customized classification approach that reveals interacting regions. Richter, Mirco deposit-license Optimized cortical subdivision for classification of Alzheimer's disease with cortical thickness Merhof, Dorit Richter, Mirco 2013-04-04T14:48:23Z 2013-02-20 Merhof, Dorit 2014-04-30T22:25:06Z Bildverarbeitung für die Medizin 2013 : Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 3. bis 5. März 2013 in Heidelberg / Hans-Peter Meinzer ... - Berlin : Springer, 2013. - S. 33-38. - ISBN 978-3-642-36479-2

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