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| Title: | Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images |
| Abstract: | Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for de- tecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical domain, based on statistical methods, content-based retrieval, clustering and re- cently also deep learning. Previous approaches towards deep unsuper- vised anomaly detection model local patches of normal anatomy with variants of Autoencoders or GANs, and detect anomalies either as out- liers in the learned feature space or from large reconstruction errors. In contrast to these patch-based approaches, we show that deep spatial au- toencoding models can be efficiently used to capture normal anatomical variability of entire 2D brain MR slices. A variety of experiments on real MR data containing MS lesions corroborates our hypothesis that we can detect and even delineate anomalies in brain MR images by sim- ply comparing input images to their reconstruction. Results show that constraints on the latent space and adversarial training can further im- prove the segmentation performance over standard deep representation learning. |
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| Type: | Project |
| Area: | Segmentation, Machine Learning |
| Status: | finished |
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