Publication Details
Skull Shape Reconstruction Using Cascaded Convolutional Networks
Španěl Michal, doc. Ing., Ph.D. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
Cranial implant design, Anatomical reconstruction, 3D shape completion,
Convolutional neural networks, Generative adversarial networks
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.
@article{BUT168170,
author="Oldřich {Kodym} and Michal {Španěl} and Adam {Herout}",
title="Skull Shape Reconstruction Using Cascaded Convolutional Networks",
journal="COMPUTERS IN BIOLOGY AND MEDICINE",
year="2020",
volume="123",
number="103886",
pages="1--9",
doi="10.1016/j.compbiomed.2020.103886",
issn="0010-4825",
url="https://www.sciencedirect.com/science/article/pii/S0010482520302365?via%3Dihub"
}