Ms. Bruna F. Silva Profile

Bruna F. Silva

at Univ of Campinas

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Proceedings Article | 13 March 2019 Presentation + Paper

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

Samuel Botter Martins, Barbara Caroline Benato, Bruna Ferreira Silva, Clarissa Lyn Yasuda, Alexandre Xavier Falcão

Proceedings Volume 10950, 109500C (2019) https://doi.org/10.1117/12.2512873

KEYWORDS: Brain, Image segmentation, Neuroimaging, Magnetic resonance imaging, Neural networks, Epilepsy, Feature extraction

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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.

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