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

Implants are an important instrument inmodernmedicine for providing patients with a higher quality of life after accidentor disease-related functional limitations. In cranial neurosurgery, reconstructive implants are primarily used to restore normal skull function and anatomical integrity after severe head trauma, resection of bone affecting tumors, or bone loss due to infection or spontaneous postoperative bone flap resorption. Patient specific implants (PSI) are custommade implants manufactured to each patient’s individual anatomical specifications, and the advent of new manufacturing techniques and materials opens the opportunity for a closer integration into the clinical routine. The following contribution aims at giving non-medical participants of the AutoImplant challenge some insight into the neurosurgical perspective on when cranial implants are needed, what the surgical procedures are, what cranioplasty methods currently are available, what criteria should be met by the implants, and where the limitations of the current manufacturing solutions lie.

[1]  Enzo Ferrante,et al.  Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders , 2019, MICCAI.

[2]  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).

[3]  Chong-Ching Chang,et al.  Custom implant design for patients with cranial defects. , 2002, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[4]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

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

[6]  J. Fandino,et al.  Intraoperative template-molded bone flap reconstruction for patient-specific cranioplasty , 2012, Neurosurgical Review.

[7]  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.

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

[9]  Nick Pears,et al.  Modelling of Orthogonal Craniofacial Profiles , 2017, J. Imaging.

[10]  Jan S. Kirschke,et al.  VerSe: A Vertebrae Labelling and Segmentation Benchmark , 2020, ArXiv.

[11]  Thomas C. Walters,et al.  Low Bit-rate Speech Coding with VQ-VAE and a WaveNet Decoder , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  Richard A. Newcombe,et al.  DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  H. Graichen,et al.  Method for quantitative assessment of acetabular bone defects , 2018, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[14]  Bjoern H Menze,et al.  Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation , 2019, MLMI@MICCAI.

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

[16]  H.A.M. Mahinda,et al.  Variability in Thickness of Human Skull Bones and Sternum – an Autopsy Experience , 2009 .

[17]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  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).

[19]  Verónica Vilaplana,et al.  Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries , 2017, Lecture Notes in Computer Science.

[20]  Mingqiang Wei,et al.  Sparse-to-Dense Multi-Encoder Shape Completion of Unstructured Point Cloud , 2020, IEEE Access.

[21]  Yang Lei,et al.  Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net. , 2019, Medical physics.

[22]  M Dujovny,et al.  Cranioplasty: cosmetic or therapeutic? , 1997, Surgical neurology.

[23]  Toby P. Breckon,et al.  GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training , 2018, ACCV.

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

[25]  Nassir Navab,et al.  Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images , 2018, BrainLes@MICCAI.

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

[27]  James G Mainprize,et al.  Biometric Morphing: A Novel Technique for the Analysis of Morphologic Outcomes After Facial Surgery , 2009, Annals of plastic surgery.

[28]  J. Vander Sloten,et al.  Design for medical rapid prototyping of cranioplasty implants , 2003 .

[29]  Paul Babyn,et al.  Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network , 2017, Journal of Digital Imaging.

[30]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[31]  Rohit Ghosh,et al.  Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans , 2018, ArXiv.

[32]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[33]  Hongwei Li,et al.  Feedback Graph Attention Convolutional Network for Medical Image Enhancement , 2020, ArXiv.

[34]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[35]  Wei Chen,et al.  Learning to predict the cosmological structure formation , 2018, Proceedings of the National Academy of Sciences.

[36]  Hans-Christian Hege,et al.  A 3D statistical shape model of the pelvic bone for segmentation , 2004, SPIE Medical Imaging.

[37]  Jan S. Kirschke,et al.  Conditioned Variational Auto-encoder for Detecting Osteoporotic Vertebral Fractures , 2019, CSI@MICCAI.

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

[39]  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.

[40]  Leonidas J. Guibas,et al.  Data-driven structural priors for shape completion , 2015, ACM Trans. Graph..

[41]  Iasonas Kokkinos,et al.  Prior-Based Coregistration and Cosegmentation , 2016, MICCAI.

[42]  Hongzhi Wang,et al.  Segmentation of anatomical structures in cardiac CTA using multi-label V-Net , 2018, Medical Imaging.

[43]  Xinyi Le,et al.  Point Encoder GAN: A deep learning model for 3D point cloud inpainting , 2020, Neurocomputing.

[44]  Diego H. Milone,et al.  Learning Deformable Registration of Medical Images with Anatomical Constraints , 2020, Neural Networks.

[45]  Sébastien Ourselin,et al.  Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.

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

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

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

[49]  Lapo Governi,et al.  A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons , 2019, Symmetry.

[50]  Xiangjian He,et al.  Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges , 2019, Journal of Digital Imaging.

[51]  Thomas Looi,et al.  Generation of normative pediatric skull models for use in cranial vault remodeling procedures , 2012, Child's Nervous System.

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

[53]  Stefano Benazzi,et al.  Comparing 3-dimensional virtual methods for reconstruction in craniomaxillofacial surgery. , 2011, Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons.

[54]  C Schaller,et al.  Cranioplasty for large skull defects with PMMA (Polymethylmethacrylate) or Tutoplast processed autogenic bone grafts. , 2007, Zentralblatt fur Neurochirurgie.

[55]  Andriy Myronenko,et al.  3D MRI brain tumor segmentation using autoencoder regularization , 2018, BrainLes@MICCAI.

[56]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[58]  Oldrich Kodym,et al.  Evaluating Deep Learning Uncertainty Measures in Cephalometric Landmark Localization , 2020, BIOIMAGING.

[59]  Ben Glocker,et al.  TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors , 2019, IEEE Transactions on Medical Imaging.

[60]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[61]  Shubham Jain,et al.  2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation , 2017, STACOM@MICCAI.

[62]  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).

[63]  A. Sarmast,et al.  Cranioplasty: Indications, procedures, and outcome – An institutional experience , 2017, Surgical neurology international.

[64]  Hans-Christian Hege,et al.  Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model , 2008, VCBM.

[65]  Mario Ceresa,et al.  Fully automatic detection and segmentation of abdominal aortic thrombus in post‐operative CTA images using Deep Convolutional Neural Networks , 2018, Medical Image Anal..

[66]  D. Rueckert,et al.  Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. , 2020, The Lancet. Digital health.

[67]  A. Raabe,et al.  What happens to the bone flap? Long-term outcome after reimplantation of cryoconserved bone flaps in a consecutive series of 92 patients , 2015, Acta Neurochirurgica.

[68]  A. Rüegg,et al.  A Generalized Kinematics Model for Three- to Five-Axis Milling Machines and Their Implementation in a CNC , 1992 .

[69]  Georg Langs,et al.  f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks , 2019, Medical Image Anal..

[70]  Young Min Kim,et al.  RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[71]  D. Sinn,et al.  Craniofacial implant surgery. , 2011, Dental clinics of North America.

[72]  Chandra Sekhar Seelamantula,et al.  Training-Free, Single-Image Super-Resolution Using a Dynamic Convolutional Network , 2018, IEEE Signal Processing Letters.

[73]  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).

[74]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[75]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[76]  Enzo Ferrante,et al.  Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders , 2020, IEEE Transactions on Medical Imaging.

[77]  Yuxiang Zhou,et al.  Deep residual dense U-Net for resolution enhancement in accelerated MRI acquisition , 2019, Medical Imaging: Image Processing.

[78]  O. Antonyshyn,et al.  A technique for intraoperative creation of patient-specific titanium mesh implants , 2015, Plastic surgery.

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

[80]  Frédo Durand,et al.  Data augmentation using learned transforms for one-shot medical image segmentation , 2019, ArXiv.

[81]  Michael Eickenberg,et al.  Machine learning for neuroimaging with scikit-learn , 2014, Front. Neuroinform..

[82]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[83]  Matthias Bethge,et al.  ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.

[84]  J. Leigh,et al.  Pre-surgical cranial implant design using the PARIS/spl trade/ prototype , 2004, IEEE Virtual Reality 2004.

[85]  Dirk Vandermeulen,et al.  CT-based robust statistical shape modeling for forensic craniofacial reconstruction , 2011, ICDP.

[86]  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.

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

[88]  Andreas Geiger,et al.  Learning 3D Shape Completion Under Weak Supervision , 2018, International Journal of Computer Vision.