Abstract
Designing a patient-specific cranial implant usually requires reconstructing the defective part of the skull using computer-aided design software, which is a tedious and time-demanding task. This lead to some recent advances in the field of automatic skull reconstruction with use of methods based on shape analysis or deep learning. The AutoImplant Challenge aims at providing a public platform for benchmarking skull reconstruction methods. The BUT submission to this challenge is based on skull alignment using landmark detection followed by a cascade of low-resolution and high-resolution reconstruction convolutional neural network. We demonstrate that the proposed method successfully reconstructs every skull in the standard test dataset and outperforms the baseline method in both overlap and distance metrics, achieving 0.920 DSC and 4.137 mm HD.
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Acknowledgements
This work was partly supported by TESCAN Medical and TESCAN 3DIM companies. We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the NVIDIA TITAN Xp GPU for this research.
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Kodym, O., Španěl, M., Herout, A. (2020). Cranial Defect Reconstruction Using Cascaded CNN with Alignment. In: Li, J., Egger, J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science(), vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_7
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DOI: https://doi.org/10.1007/978-3-030-64327-0_7
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