Abstract
The hyperdense artery sign (HAS) in cranial non-contrast computed tomography (NCCT) is one of the earliest indicators of an ischemic stroke. We present a deep-learning-based method which incorporates symptomatic information to segment these findings. Our dataset consists of 114 NCCT scans. We include the entire cerebrovascular system, with most occlusions appearing in the M1 or M2 segment of the middle cerebral artery (MCA). Our method is based on the nnUNet framework. We evaluated the inclusion of the information regarding the side of the body on which the stroke symptoms occurred by encoding it in the second input channel. Doing so enhanced nnUNet’s Dice score on the 34 test cases from 0.44 to 0.52. A Dice score of > 0.1, indicating that the thrombus was located correctly, was found in 76 % of the cases. Thereby strong differences in the performance depending on the type of occlusion were observed: for M1 and M2 occlusions a Dice score of > 0.1 was present in 89 % and 73 % of the test cases, whereas the value for the other occlusions was only 25 %. Our study not only confirms the general suitability of the nnUNet for HAS segmentation but also proposes an effective method for incorporating symptom information to enhance the network’s performance. To the best of our knowledge, we are the first to incorporate individual clinical information to enhance HAS segmentation.
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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Ertl, A. et al. (2023). Automated Thrombus Segmentation in Stroke NCCT Incorporating Clinical Data. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_33
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DOI: https://doi.org/10.1007/978-3-658-41657-7_33
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