Abstract
Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was recently highlighted by ACR Data Science Institute. In this paper we introduce a novel deep learning based approach for the problem of MLS detection, which exploits task-specific structural knowledge. We evaluate our method on a large dataset containing heterogeneous images with significant MLS and show that its mean error approaches the inter-expert variability. Finally, we show the robustness of our approach by validating it on an external dataset, acquired during routine clinical practice.
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Change history
24 October 2019
The chapter titled “Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection” was revised. The names of two authors were spelled incorrectly and the grant number was missing the final digit. This was corrected.
The original version of this book was revised. Due to an error, the volume editor’s affiliation “ETH Zurich” appeared on SpringerLink instead of his name “Ender Konukoglu.” This was fixed.
Notes
- 1.
Full code for training and inference is available at GitHub: https://github.com/neuro-ml/midline-shift-detection.
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Acknowledgements
The development of the interpretable algorithm (done by M. Pisov and M. Goncharov) was supported by the Russian Science Foundation grant 17-11-01390.
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Pisov, M. et al. (2019). Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection. In: Suzuki, K., et al. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. ML-CDS IMIMIC 2019 2019. Lecture Notes in Computer Science(), vol 11797. Springer, Cham. https://doi.org/10.1007/978-3-030-33850-3_4
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