Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks

K. Standvoss; T. Crijns; L. Goerke; D. Janssen; S. Kern; T. van Niedek; J. van Vugt; N. Alfonso Burgos; E. J. Gerritse; J. Mol; D. van de Vooren; M. Ghafoorian; T. L. A. van den Heuvel; R. Manniesing

doi:10.1117/12.2294016

27 February 2018 Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks

K. Standvoss, T. Crijns, L. Goerke, D. Janssen, S. Kern, T. van Niedek, J. van Vugt, N. Alfonso Burgos, E. J. Gerritse, J. Mol, D. van de Vooren, M. Ghafoorian, T. L. A. van den Heuvel, R. Manniesing

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105751D (2018) https://doi.org/10.1117/12.2294016
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

The number and location of cerebral microbleeds (CMBs) in patients with traumatic brain injury (TBI) is important to determine the severity of trauma and may hold prognostic value for patient outcome. However, manual assessment is subjective and time-consuming due to the resemblance of CMBs to blood vessels, the possible presence of imaging artifacts, and the typical heterogeneity of trauma imaging data. In this work, we present a computer aided detection system based on 3D convolutional neural networks for detecting CMBs in 3D susceptibility weighted images. Network architectures with varying depth were evaluated. Data augmentation techniques were employed to improve the networks’ generalization ability and selective sampling was implemented to handle class imbalance. The predictions of the models were clustered using a connected component analysis. The system was trained on ten annotated scans and evaluated on an independent test set of eight scans. Despite this limited data set, the system reached a sensitivity of 0.87 at 16.75 false positives per scan (2.5 false positives per CMB), outperforming related work on CMB detection in TBI patients.

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K. Standvoss, T. Crijns, L. Goerke, D. Janssen, S. Kern, T. van Niedek, J. van Vugt, N. Alfonso Burgos, E. J. Gerritse, J. Mol, D. van de Vooren, M. Ghafoorian, T. L. A. van den Heuvel, and R. Manniesing "Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751D (27 February 2018); https://doi.org/10.1117/12.2294016

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