A baseline approach for autoimplant: the MICCAI 2020 cranial implant design challenge

Jianning Li, Antonio Pepe, Christina Schwarz-Gsaxner, Gord von Campe, Jan Egger

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


In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) – the cranial implant design challenge, which can be formulated as a volumetric shape learning task. In this task, the defective skull, the complete skull and the cranial implant are represented as binary voxel grids. To accomplish this task, the implant can be either reconstructed directly from the defective skull or obtained by taking the difference between a defective skull and a complete skull. In the latter case, a complete skull has to be reconstructed given a defective skull, which defines a volumetric shape completion problem. Our baseline approach for this task is based on the former formulation, i.e., a deep neural network is trained to predict the implants directly from the defective skulls. The approach generates high-quality implants in two steps: First, an encoder-decoder network learns a coarse representation of the implant from downsampled, defective skulls; The coarse implant is only used to generate the bounding box of the defected region in the original high-resolution skull. Second, another encoder-decoder network is trained to generate a fine implant from the bounded area. On the test set, the proposed approach achieves an average dice similarity score (DSC) of 0.8555 and Hausdorff distance (HD) of 5.1825 mm. The codes are available at https://github.com/Jianningli/autoimplant.
Original languageEnglish
Title of host publicationMultimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures
Subtitle of host publication10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsTanveer Syeda-Mahmood, Klaus Drechsler, Hayit Greenspan, Anant Madabhushi, Alexandros Karargyris, Cristina Oyarzun Laura, Stefan Wesarg, Marius George Linguraru, Raj Shekhar, Marius Erdt, Miguel Ángel González Ballester
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)978-3-030-60946-7
ISBN (Print)978-3-030-60945-0
Publication statusPublished - 1 Jan 2020
Event2020 Workshop on Clinical Image-Based Procedures: in Conjunction with MICCAI 2020 - Virtual, Virtuell, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2020 Workshop on Clinical Image-Based Procedures
Abbreviated titleCLIP 2020
Internet address


  • deep learning
  • skull reconstruction
  • shape completion
  • Deep learning
  • Shape learning
  • Cranial implant design
  • Cranioplasty
  • Skull reconstruction
  • Volumetric shape completion

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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