High-Resolution Cranial Implant Prediction via Patch-Wise Training

Yuan Jin, Jianning Li, Jan Egger

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


In this study, we proposed two methods for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge. The shape of the implant is predicted based on the inputted defective skull. This task can be accomplished either by directly predicting the implant with the defective skull, or indirectly rebuilding the complete skull and then taking the difference between the defective and complete skulls. In our work, a deep learning model is applied to automatically predict the implant. In order to solve the problem that high resolution images can often not be directly inputted to the deep learning model, two proposed methods of resize and patch-based are examined. On the test set, the proposed resize method achieves an average dice similarity score (DSC) of 0.7350 and a Hausdorff distance (HD) of 7.2425 mm, while the proposed patch-based method achieves an average DSC of 0.8887 and a HD of 5.5339 mm.
Original languageEnglish
Title of host publicationTowards the Automatization of Cranial Implant Design in Cranioplasty - First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsJianning Li, Jan Egger
PublisherSpringer, Cham
Pages94 -103
Number of pages10
ISBN (Print)9783030643263
Publication statusE-pub ahead of print - 4 Dec 2020
Event1st Automatization of Cranial Implant Design in Cranioplasty Challenge: AutoImplant 2020 - Virtual, Lima, Peru
Duration: 8 Oct 2020 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12439 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st Automatization of Cranial Implant Design in Cranioplasty Challenge
CityVirtual, Lima
Period8/10/20 → …
Internet address


  • AutoImplant
  • Cranioplasty
  • Deep learning
  • Shape completion
  • Super-resolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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