Publication detail

Cranial Defect Reconstruction Using Cascaded CNN with Alignment

KODYM, O. ŠPANĚL, M. HEROUT, A.

Original Title

Cranial Defect Reconstruction Using Cascaded CNN with Alignment

Type

conference paper

Language

English

Original 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.

Keywords

Alingment, Computer aided design, Convolutional neural networks, Deep learnin, Defects, Medical computing, Statistical tests

Authors

KODYM, O.; ŠPANĚL, M.; HEROUT, A.

Released

8. 10. 2020

Publisher

Springer Nature Switzerland AG

Location

Lima

ISBN

978-3-030-64326-3

Book

Towards the Automatization of Cranial Implant Design in Cranioplasty

Edition

Lecture Notes in Computer Science

Pages from

56

Pages to

64

Pages count

9

URL

https://link.springer.com/chapter/10.1007/978-3-030-64327-0_7

BibTex

@inproceedings{BUT168486,
  author="Oldřich {Kodym} and Michal {Španěl} and Adam {Herout}",
  title="Cranial Defect Reconstruction Using Cascaded CNN with Alignment",
  booktitle="Towards the Automatization of Cranial Implant Design in Cranioplasty",
  year="2020",
  series="Lecture Notes in Computer Science",
  pages="56--64",
  publisher="Springer Nature Switzerland AG",
  address="Lima",
  doi="10.1007/978-3-030-64327-0\{_}7",
  isbn="978-3-030-64326-3",
  url="https://link.springer.com/chapter/10.1007/978-3-030-64327-0_7"
}