Publication detail

Deep Learning for Cranioplasty in Clinical Practice: Going from Synthetic to Real Patient Data

KODYM, O. ŠPANĚL, M. HEROUT, A.

Original Title

Deep Learning for Cranioplasty in Clinical Practice: Going from Synthetic to Real Patient Data

Type

journal article in Web of Science

Language

English

Original Abstract

Correct virtual reconstruction of a de- fective skull is a prerequisite for successful cranioplasty and its automatization has the potential for accelerat- ing and standardizing the clinical workflow. This work provides a deep learning-based method for the recon- struction of a skull shape and cranial implant design on clinical data of patients indicated for cranioplasty. The method is based on a cascade of multi-branch vol- umetric CNNs that enables simultaneous training on two different types of cranioplasty ground-truth data: the skull patch, which represents the exact shape of the missing part of the original skull, and which can be eas- ily created artificially from healthy skulls, and expert- designed cranial implant shapes that are much harder to acquire. The proposed method reaches an average surface distance of the reconstructed skull patches of 0.67 mm on a clinical test set of 75 defective skulls. It also achieves a 12% reduction of a newly proposed de- fect border Gaussian curvature error metric, compared to a baseline model trained on synthetic data only. Ad- ditionally, it produces directly 3D printable cranial im- plant shapes with a Dice coefficient 0.88 and a surface error of 0.65 mm. The outputs of the proposed skull reconstruction method reach good quality and can be considered for use in semi- or fully automatic clinical cranial implant design workflows.

Keywords

Cranioplasty; Skull Reconstruction; Cranial Implant Design; 3D Convolutional Neural Networks

Authors

KODYM, O.; ŠPANĚL, M.; HEROUT, A.

Released

1. 10. 2021

ISBN

0010-4825

Periodical

COMPUTERS IN BIOLOGY AND MEDICINE

Year of study

137

Number

104766

State

United States of America

Pages from

1

Pages to

10

Pages count

10

URL

https://www.sciencedirect.com/science/article/abs/pii/S0010482521005606?via%3Dihub

BibTex

@article{BUT175781,
  author="Oldřich {Kodym} and Michal {Španěl} and Adam {Herout}",
  title="Deep Learning for Cranioplasty in Clinical Practice: Going from Synthetic to Real Patient Data",
  journal="COMPUTERS IN BIOLOGY AND MEDICINE",
  year="2021",
  volume="137",
  number="104766",
  pages="1--10",
  doi="10.1016/j.compbiomed.2021.104766",
  issn="0010-4825",
  url="https://www.sciencedirect.com/science/article/abs/pii/S0010482521005606?via%3Dihub"
}