Abstract
Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. Deep generative models, such as Generative Adversarial Networks (GANs), can be exploited to capture anatomical variability. Consequently, any outlier (i.e., sample falling outside of the learned distribution) can be detected as an abnormality in an unsupervised fashion. By using this method, we can not only detect expected or known lesions, but we can even unveil previously unrecognized biomarkers. To the best of our knowledge, this study exemplifies the first AD approach that can efficiently handle volumetric data and detect 3D brain anomalies in one single model. Our proposal is a volumetric and high-detail extension of the 2D f-AnoGAN model obtained by combining a state-of-the-art 3D GAN with refinement training steps. In experiments using non-contrast computed tomography images from traumatic brain injury (TBI) patients, the model detects and localizes TBI abnormalities with an area under the ROC curve of \(\sim \)75\(\%\). Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt). The method has potential for rapidly labeling abnormalities in massive imaging datasets, as well as identifying new biomarkers.
CENTER-TBI participants and investigators are listed at the end of the supplementary material.
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Acknowledgments
JSV received an Erasmus\(\texttt {+}\) scholarship from the Erasmus Mundus Joint Master Degree in Medical Imaging and Applications (MAIA), a programme funded by the Erasmus\(\texttt {+}\) programme of the European Union (EU grant 20152491). This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement TRABIT No 765148. DR is supported by an innovation mandate of Flanders Innovation & Entrepreneurship (VLAIO). Data used in preparation of this manuscript were obtained in the context of CENTER-TBI, a large collaborative project with the support of the European Union 7th Framework program (EC grant 602150). Additional funding was obtained from the Hannelore Kohl Stiftung (Germany), from OneMind (USA) and from Integra LifeSciences Corporation (USA).
We thank Charlotte Timmermans and Nathan Vanalken for performing the manual TBI segmentations.
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Simarro Viana, J., de la Rosa, E., Vande Vyvere, T., Robben, D., Sima, D.M., Investigators, CT.P.a. (2021). Unsupervised 3D Brain Anomaly Detection. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_13
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