Sparse Convolutional Neural Network for Skull Reconstruction

Artem Kroviakov, Jianning Li*, Jan Egger

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Patient-specific implant (PSI) design is a challenging task and requires a specialist, who will spend a significant amount of time using computer aided design tools for implant creation, since patient-specific skull features have to be accounted for. Automating this process could potentially allow intraoperative PSI availability at a relatively low cost. This work proposes to use a 3D Sparse Convolutional Neural Network (SCNN) to reconstruct complete skulls given defective skulls (i.e., skull shape completion) and extract implants by taking the difference between them. With the help of recently published methods for sparse convolutions, it is now possible to avoid the downsampling of the whole skull volume, which is required for conventional dense 3D CNN applications proposed previously. Thus, the SCNN-based approach allows to preserve the original skull geometry. The proposed pipeline includes a supervised SCNN autoencoder network with data preprocessing steps, which further exploit the sparse nature of a skull scan. The best setup in our experiments achieves a Dice Score (DS) of 85.4%, a Border Dice Score of 94.6%, Hausdorff Distance (HD) of 4.91 and 95th percentile HD of 2.64 on the dataset for Task 3 of the AutoImplant 2021 challenge (https://autoimplant2021.grand-challenge.org/ ). The results are comparable with a dense CNN counterpart which has significantly more parameters and requires downsampling and cropping of the skull image on 6GB GPUs. The code is publicly available at https://github.com/akroviakov/SparseSkullCompletion.

Original languageEnglish
Title of host publicationTowards the Automatization of Cranial Implant Design in Cranioplasty 2 - Second Challenge, AutoImplant MICCAI 2021, Proceedings
EditorsJianning Li, Jan Egger, Jianning Li, Jan Egger
PublisherSpringer Science and Business Media Deutschland GmbH
Pages80-94
Number of pages15
ISBN (Print)9783030926519
DOIs
Publication statusPublished - 2021
Event2nd Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2021 - Virtuell, Austria
Duration: 1 Oct 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13123 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention
Abbreviated titleMICCAI 2021
Country/TerritoryAustria
CityVirtuell
Period1/10/211/10/21

Keywords

  • Shape completion
  • Sparse Convolutional Neural Network (SCNN)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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