AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.

[1]  Richard K. Beatson,et al.  Surface interpolation with radial basis functions for medical imaging , 1997, IEEE Transactions on Medical Imaging.

[2]  Peter Liepa,et al.  Filling Holes in Meshes , 2003, Symposium on Geometry Processing.

[3]  Alla Sheffer,et al.  Template-based mesh completion , 2005, SGP '05.

[4]  Martin Styner,et al.  Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM. , 2006, The insight journal.

[5]  Michael Figl,et al.  Sample Sufficiency and PCA Dimension for Statistical Shape Models , 2008, ECCV.

[6]  Hans Lamecker,et al.  Variational and statistical shape modeling for 3D geometry reconstruction , 2008 .

[7]  L. Nickels.  World's first patient-specific jaw implant , 2012 .

[8]  F. Servadei,et al.  Use of “custom made” porous hydroxyapatite implants for cranioplasty: postoperative analysis of complications in 1549 patients , 2013, Surgical neurology international.

[9]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[10]  J. Simard,et al.  Complications Associated with Decompressive Craniectomy: A Systematic Review , 2015, Neurocritical Care.

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Leonidas J. Guibas,et al.  ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.

[13]  Carl Doersch,et al.  Tutorial on Variational Autoencoders , 2016, ArXiv.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Dieter Schmalstieg,et al.  Computer-aided planning and reconstruction of cranial 3D implants , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[17]  Jan Egger,et al.  Computer-aided implant design for the restoration of cranial defects , 2017, Scientific Reports.

[18]  Zhen Li,et al.  High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Florian Probst,et al.  Planning of skull reconstruction based on a statistical shape model combined with geometric morphometrics , 2017, International Journal of Computer Assisted Radiology and Surgery.

[20]  Stefan Schlager,et al.  Virtual reconstruction of midface defects using statistical shape models. , 2017, Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery.

[21]  Dieter Schmalstieg,et al.  Interactive reconstructions of cranial 3D implants under MeVisLab as an alternative to commercial planning software , 2017, PloS one.

[22]  Matthias Nießner,et al.  Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Dichen Li,et al.  Custom design and biomechanical analysis of 3D-printed PEEK rib prostheses , 2018, Biomechanics and modeling in mechanobiology.

[24]  Martial Hebert,et al.  PCN: Point Completion Network , 2018, 2018 International Conference on 3D Vision (3DV).

[25]  Ana Rita André Morais.  Automated computer-aided design of cranial implants: a deep learning approach , 2018 .

[26]  Jan Egger,et al.  Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder , 2019, WorldCIST.

[27]  Viacheslav V. Voronin,et al.  Medical Image Inpainting Using Multi-Scale Patches and Neural Networks Concepts , 2019, IOP Conference Series: Materials Science and Engineering.

[28]  Lapo Governi,et al.  A Semi-Automatic Hybrid Approach for Defective Skulls Reconstruction , 2019, Proceedings of CAD'19.

[29]  Pourya Shamsolmoali,et al.  A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Fuessinger Marc Anton,et al.  Virtual reconstruction of bilateral midfacial defects by using statistical shape modeling. , 2019, Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery.

[31]  J. Rosenfeld,et al.  Complications After Decompressive Craniectomy and Cranioplasty , 2019, Complications in Neurosurgery.

[32]  Jianning Li,et al.  Towards the Automatization of Cranial Implant Design in Cranioplasty: First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings , 2020, AutoImplant@MICCAI.

[33]  Pierrick Coupé,et al.  Blind MRI Brain Lesion Inpainting Using Deep Learning , 2020, SASHIMI@MICCAI.

[34]  Jianning Li,et al.  High-Resolution Cranial Implant Prediction via Patch-Wise Training , 2020, AutoImplant@MICCAI.

[35]  Jan Egger,et al.  Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images , 2020, ArXiv.

[36]  Jan S. Kirschke,et al.  Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement , 2020, AutoImplant@MICCAI.

[37]  Yu-Shen Liu,et al.  Point Cloud Completion by Skip-Attention Network With Hierarchical Folding , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Jianning Li.  Deep Learning for Cranial Defect Reconstruction , 2020 .

[39]  Haochen Shi,et al.  Cranial Implant Design Through Multiaxial Slice Inpainting Using Deep Learning , 2020, AutoImplant@MICCAI.

[40]  Christina Gsaxner,et al.  A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge , 2020, ML-CDS/CLIP@MICCAI.

[41]  Gord von Campe,et al.  Patient Specific Implants (PSI) - Cranioplasty in the Neurosurgical Clinical Routine , 2020, AutoImplant@MICCAI.

[42]  Stefan Zachow,et al.  Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks , 2020, AutoImplant@MICCAI.

[43]  Yuru Pei,et al.  Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks , 2020, MLMI@MICCAI.

[44]  Enzo Ferrante,et al.  Cranial Implant Design via Virtual Craniectomy with Shape Priors , 2020, AutoImplant@MICCAI.

[45]  David G. Ellis,et al.  Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge , 2020, AutoImplant@MICCAI.

[46]  Adam Herout,et al.  Skull shape reconstruction using cascaded convolutional networks , 2020, Comput. Biol. Medicine.

[47]  Jianning Li,et al.  Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge , 2020, AutoImplant@MICCAI.

[48]  Sergios Gatidis,et al.  ipA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging , 2019, 2020 IEEE International Conference on Image Processing (ICIP).

[49]  James G. Mainprize,et al.  Shape Completion by U-Net: An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge , 2020, AutoImplant@MICCAI.

[50]  Christina Gsaxner,et al.  An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design , 2020, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling.

[51]  Yujun Li,et al.  Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization , 2020, AutoImplant@MICCAI.

[52]  Enzo Ferrante,et al.  Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy , 2020, MICCAI.

[53]  Adam Herout,et al.  Cranial Defect Reconstruction Using Cascaded CNN with Alignment , 2020, AutoImplant@MICCAI.

[54]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[55]  Lu Sheng,et al.  Morphing and Sampling Network for Dense Point Cloud Completion , 2019, AAAI.