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Modeling normal brain asymmetry in MR images applied to anomaly detection without segmentation and data annotation
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Paper Abstract

While the human brain presents natural structural asymmetries between left and right hemispheres in MR images, most neurological diseases are associated with abnormal brain asymmetries. Due to the great variety of such anomalies, we present a framework to model normal structural brain asymmetry from control subjects only, independent of the neurological disease. The model dismisses data annotation by exploiting generative deep neural networks and one-class classifiers. We also propose a patch-based model to localize volumes of interest with reduced background sizes around selected brain structures and a one-class classifier based on an optimum-path forest. This model makes the framework independent of segmentation, which may fail, especially in abnormal images, or may not be available for a given structure. We validate the first method to the detection of abnormal hippocampal asymmetry using distinct groups of Epilepsy patients and testing controls. The results of validation using the original feature space and a two-dimensional space based on non-linear projection show the potential to extend the framework for abnormal asymmetry detection in other parts of the brain and develop intelligent and interactive virtual environments. For instance, the approach can be used for screening, inspection, and annotation of the detected anomaly type, allowing the development of CADx systems.

Paper Details

Date Published: 13 March 2019
PDF: 10 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500C (13 March 2019); doi: 10.1117/12.2512873
Show Author Affiliations
Samuel Botter Martins, Univ. of Campinas (Brazil)
Federal Institute of Education, Science, and Technology of São Paulo (Brazil)
Barbara Caroline Benato, Univ. of Campinas (Brazil)
Bruna Ferreira Silva, Univ. of Campinas (Brazil)
Clarissa Lyn Yasuda, Univ. of Campinas (Brazil)
Alexandre Xavier Falcão, Univ. of Campinas (Brazil)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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