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"
}

Documents

cranio_cbm_2021.pdf