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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.