Publication detail
Skull Shape Reconstruction Using Cascaded Convolutional Networks
KODYM, O. ŠPANĚL, M. HEROUT, A.
Original Title
Skull Shape Reconstruction Using Cascaded Convolutional Networks
English Title
Skull Shape Reconstruction Using Cascaded Convolutional Networks
Type
journal article in Web of Science
Language
en
Original Abstract
Designing a cranial implant to restore the protective and aesthetic function of the patient's skull is a challenging process that requires a substantial amount of manual work, even for an experienced clinician. While computer-assisted approaches with various levels of required user interaction exist to aid this process, they are usually only validated on either a single type of simple synthetic defect or a very limited sample of real defects. The work presented in this paper aims to address two challenges: (i) design a fully automatic 3D shape reconstruction method that can address diverse shapes of real skull defects in various stages of healing and (ii) to provide an open dataset for optimization and validation of anatomical reconstruction methods on a set of synthetically broken skull shapes. We propose an application of the multi-scale cascade architecture of convolutional neural networks to the reconstruction task. Such an architecture is able to tackle the issue of trade-off between the output resolution and the receptive field of the model imposed by GPU memory limitations. Furthermore, we experiment with both generative and discriminative models and study their behavior during the task of anatomical reconstruction. The proposed method achieves an average surface error of 0.59 for our synthetic test dataset with as low as 0.48 for unilateral defects of parietal and temporal bone, matching state-of-the-art performance while being completely automatic. We also show that the model trained on our synthetic dataset is able to reconstruct real patient defects.
English abstract
Designing a cranial implant to restore the protective and aesthetic function of the patient's skull is a challenging process that requires a substantial amount of manual work, even for an experienced clinician. While computer-assisted approaches with various levels of required user interaction exist to aid this process, they are usually only validated on either a single type of simple synthetic defect or a very limited sample of real defects. The work presented in this paper aims to address two challenges: (i) design a fully automatic 3D shape reconstruction method that can address diverse shapes of real skull defects in various stages of healing and (ii) to provide an open dataset for optimization and validation of anatomical reconstruction methods on a set of synthetically broken skull shapes. We propose an application of the multi-scale cascade architecture of convolutional neural networks to the reconstruction task. Such an architecture is able to tackle the issue of trade-off between the output resolution and the receptive field of the model imposed by GPU memory limitations. Furthermore, we experiment with both generative and discriminative models and study their behavior during the task of anatomical reconstruction. The proposed method achieves an average surface error of 0.59 for our synthetic test dataset with as low as 0.48 for unilateral defects of parietal and temporal bone, matching state-of-the-art performance while being completely automatic. We also show that the model trained on our synthetic dataset is able to reconstruct real patient defects.
Keywords
Cranial implant design, Anatomical reconstruction, 3D shape completion, Convolutional neural networks, Generative adversarial networks
Released
26.08.2020
Publisher
NEUVEDEN
Location
NEUVEDEN
ISBN
0010-4825
Periodical
COMPUTERS IN BIOLOGY AND MEDICINE
Year of study
123
Number
103886
State
US
Pages from
1
Pages to
9
Pages count
9
URL
Documents
BibTex
@article{BUT168170,
author="Oldřich {Kodym} and Michal {Španěl} and Adam {Herout}",
title="Skull Shape Reconstruction Using Cascaded Convolutional Networks",
annote="Designing a cranial implant to restore the protective and aesthetic function of
the patient's skull is a challenging process that requires a substantial amount
of manual work, even for an experienced clinician. While computer-assisted
approaches with various levels of required user interaction exist to aid this
process, they are usually only validated on either a single type of simple
synthetic defect or a very limited sample of real defects. The work presented in
this paper aims to address two challenges: (i) design a fully automatic 3D shape
reconstruction method that can address diverse shapes of real skull defects in
various stages of healing and (ii) to provide an open dataset for optimization
and validation of anatomical reconstruction methods on a set of synthetically
broken skull shapes.
We propose an application of the multi-scale cascade architecture of
convolutional neural networks to the reconstruction task. Such an architecture is
able to tackle the issue of trade-off between the output resolution and the
receptive field of the model imposed by GPU memory limitations. Furthermore, we
experiment with both generative and discriminative models and study their
behavior during the task of anatomical reconstruction.
The proposed method achieves an average surface error of 0.59 for our synthetic
test dataset with as low as 0.48 for unilateral defects of parietal and temporal
bone, matching state-of-the-art performance while being completely automatic. We
also show that the model trained on our synthetic dataset is able to reconstruct
real patient defects.",
address="NEUVEDEN",
chapter="168170",
doi="10.1016/j.compbiomed.2020.103886",
edition="NEUVEDEN",
howpublished="online",
institution="NEUVEDEN",
number="103886",
volume="123",
year="2020",
month="august",
pages="1--9",
publisher="NEUVEDEN",
type="journal article in Web of Science"
}