Title: Unsupervised anomaly detection of paranasal anomalies in the maxillary sinus. <em>Medical Imaging 2023: Computer-Aided Diagnosis</em>
Written by: D. Bhattacharya and F. Behrendt and B. T. Becker and D. Beyersdorff and E. Petersen and M. Petersen and B. Cheng and D. Eggert and C. Betz and A. S. Hoffmann and A. Schlaefer
in: (2023).
Volume: <strong>12465</strong>. Number:
on pages: 124651B
Editor: In Khan M. Iftekharuddin and Weijie Chen (Eds.)
Publisher: SPIE:
how published:
Organization: International Society for Optics and Photonics
DOI: 10.1117/12.2651525
URL: https://doi.org/10.1117/12.2651525

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Abstract: Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively.

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