Abstract #2399

Unsupervised reconstruction based anomaly detection using a Variational Auto Encoder

Soumick Chatterjee^1,2,3, Alessandro Sciarra^1,4, Max Dünnwald^3,4, Shubham Kumar Agrawal³, Pavan Tummala³, Disha Setlur³, Aman Kalra³, Aishwarya Jauhari³, Steffen Oeltze-Jafra^4,5,6, Oliver Speck^1,5,6,7, and Andreas Nürnberger^2,3,6

¹Department of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, ²Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, ³Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, ⁴MedDigit, Department of Neurology, Medical Faculty, University Hopspital, Magdeburg, Germany, ⁵German Centre for Neurodegenerative Diseases, Magdeburg, Germany, ⁶Center for Behavioral Brain Sciences, Magdeburg, Germany, ⁷Leibniz Institute for Neurobiology, Magdeburg, Germany

While commonly used approach for disease localization, we propose an approach to detect anomalies by differentiating them from reliable models of anatomies without pathologies. The method is based on a Variational Auto Encoder to learn the anomaly free distribution of the anatomy and a novel image subtraction approach to obtain pixel-precise segmentation of the anomalous regions. The proposed model has been trained with the MOOD dataset. Evaluation is done on BraTS 2019 dataset and a subset of the MOOD, which contain anomalies to be detected by the model.

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