Abstract
We propose a hybrid segmentation pipeline based on the autoencoders’ capability of anomaly detection. To this end, we, first, introduce a new augmentation technique to generate synthetic paired images. Gaining advantage from the paired images, we propose a Normal Appearance Autoencoder (NAA) that is able to remove tumors and thus reconstruct realistic-looking, tumor-free images. After estimating the regions where the abnormalities potentially exist, a segmentation network is guided toward the candidate region. We tested the proposed pipeline on the BraTS 2019 database. The preliminary results indicate that the proposed model improved the segmentation accuracy of brain tumor subregions compared to the U-Net model.
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Acknowledgement
This study was supported by the Swedish Childhood Cancer Foundation (grant no. MT2016-0016), the Swedish innovation agency Vinnova (grant no. 2017-01247) and the Swedish Research Council (VR) (grant no. 2018-04375).
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Astaraki, M., Wang, C., Carrizo, G., Toma-Dasu, I., Smedby, Ö. (2020). Multimodal Brain Tumor Segmentation with Normal Appearance Autoencoder. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_31
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