Ms. Sina Walluscheck Profile

Sina Walluscheck

at Fraunhofer MEVIS Institute for Digital Medicine

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Proceedings Article | 7 April 2023 Presentation + Paper

Unsupervised learning of healthy anatomy for anomaly detection in brain CT scans

Sina Walluscheck, Luca Canalini, Jan Klein, Stefan Heldmann

Proceedings Volume 12465, 1246504 (2023) https://doi.org/10.1117/12.2653889

KEYWORDS: Computed tomography, Brain, Head, Neuroimaging, Tumors, CT reconstruction, Anatomy, Deep learning, Damage detection

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Automatic detection of abnormalities to assist radiologists in acute and screening scenarios has become a particular focus in medical imaging research. Various approaches have been proposed for the detection of anomalies in magnetic resonance (MR) data, but very little work has been done for computed tomography (CT). As far as we know, there is no satisfactory approach for anomaly detection in CT brain images. We present a novel unsupervised deep learning approach to generate a normal representation (without anomalies) of CT head scans that we use to discriminate between healthy and abnormal images. In the first step, we train a GAN with 1000 healthy CT scans to generate normal head images. Subsequently, we attach an encoder to the generator and train the auto encoder network to reconstruct healthy anatomy from new input images. The auto encoder is pre-trained with generated images using a perceptual loss function. When applied to abnormal scans, the reconstructed healthy output is then used to detect anomalies by computing the Mean Squared Error between input and output image. We evaluate our slice-wise anomaly detection on 250 test images including hemorrhages and tumors. Our approach achieves an area under receiver operating characteristic curve (AUC) of 0.90 with 85.8% sensitivity and 85.5% precision without requiring large training data sets or labeled anomaly data. Therefore, our method discriminates between normal and abnormal CT scans with good accuracy.

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