MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
暂无分享,去创建一个
Bjoern H Menze | Moon S Kim | David G. Ellis | M. Witjes | Ping Luo | M. Hatt | M. Urschler | M. Reyes | Edmond Boyer | C. Davatzikos | C. Grana | H. Lamecker | Dženan Zukić | J. Egger | Tom Kamiel Magda Vercauteren | A. Rekik | K. Tserpes | A. Lalande | F. Nensa | R. Frayne | V. Andrearczyk | Xiaojun Chen | Yannick Suter | A. Depeursinge | N. Heller | A. Sekuboyina | Kathrin Krieger | A. Campilho | M. Gunzer | Enrico Nasca | Antonio Pepe | J. Kleesiek | E. Garza-Villarreal | Jianning Li | C. Wachinger | Hongwei Li | Jun Ma | N. Shusharina | S. Beier | E. Vereecke | Constantin Seibold | Carlos A. Ferreira | H. Liebl | S. Gatidis | B. Paniagua | E. Audenaert | R. Souza | L. Rittner | Jianxu Chen | M. Aizenberg | M. Balzer | J. Shapey | R. Dorent | G. Melito | V. Badeli | T. Balducci | P. Hoyer | M. Fink | Michael Kamp | A. Nuernberger | P. Langenhuizen | Federico Bolelli | H. Salehi | T. Maal | Christina Gsaxner | Yuan Jin | A. Kujawa | Guilherme Aresta | S. Cornelissen | Yao Zhang | António Cunha | F. Jonske | Oliver Basu | Moritz Rempe | S. Chatterjee | E. D. L. Rosa | F. Hoelzle | M. Lindo | C. Krebs | R. Gharleghi | André Ferreira | V. Alves | Shireen Elhabian | A. Jaus | B. Puladi | A. Ben-Hamadou | Alexandra Brehmer | J. Pedrosa | Yuanfeng Ji | Jana Fragemann | S. Pujades | T. V. Leeuwen | J. Wasserthal | Luc Duong | F. Bahnsen | L. Podleska | Jan S. Kirschke | Gijs Luijten | D. Angeles-Valdez | Maximilian Loeffler | Luca Lumetti | A. Santos | Amr Abourayya | Jose A Garcia | A. R. Memon | Narmada Ambigapathy | Naida Solak | Patrich Ferndinand Christ | T. Kuestner | Rainer Roehrig | Amin Dada | Stanislav Malorodov | Fabian Hoerst | Lukas Heine | J. Keyl
[1] Jae Won Choi,et al. Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge , 2023, ArXiv.
[2] R. Stiefelhagen,et al. Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines , 2023, ArXiv.
[3] L. Celi,et al. A guide to sharing open healthcare data under the General Data Protection Regulation , 2023, Scientific data.
[4] David G. Ellis,et al. Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge , 2023, Medical Image Anal..
[5] Moon S Kim,et al. Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling , 2023, ArXiv.
[6] Edmond Boyer,et al. 3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge , 2023, ArXiv.
[7] Stephan J. Garbin,et al. BlendFields: Few-Shot Example-Driven Facial Modeling , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Ala I. Al-Fuqaha,et al. Privacy-preserving artificial intelligence in healthcare: Techniques and applications , 2023, Comput. Biol. Medicine.
[9] Helena R. Torres,et al. Why is the Winner the Best? , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Chen Change Loy,et al. CelebV-Text: A Large-Scale Facial Text-Video Dataset , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] A. Sowmya,et al. Annotated computed tomography coronary angiogram images and associated data of normal and diseased arteries , 2023, Scientific Data.
[12] C. Grana,et al. Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm , 2023, Applied Sciences.
[13] Menghan Xia,et al. CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Anne L. Martel,et al. Biomedical image analysis competitions: The state of current participation practice , 2022, ArXiv.
[15] R. Wiest,et al. The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation , 2022, Scientific data.
[16] A. Schwing,et al. SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation , 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Yuan Gong,et al. 3D GAN Inversion with Facial Symmetry Prior , 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] G. Shrimal,et al. AI-based Medical Image Inspection for Patient’s Racial Identity Recognition , 2022, 2022 International Conference on Futuristic Technologies (INCOFT).
[19] Edmond Boyer,et al. Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans , 2022, ArXiv.
[20] B. Schölkopf,et al. A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions , 2022, Scientific Data.
[21] J. Egger,et al. Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder , 2022, MAD@MICCAI.
[22] H. Hemmati,et al. A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation , 2022, Frontiers in Neuroinformatics.
[23] J. Egger,et al. The HoloLens in Medicine: A systematic Review and Taxonomy , 2022, Medical Image Anal..
[24] D. Štern,et al. Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge , 2022, Medical Image Anal..
[25] A. Philippakis,et al. Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk , 2022, npj Digital Medicine.
[26] K. Jia,et al. Masked Surfel Prediction for Self-Supervised Point Cloud Learning , 2022, ArXiv.
[27] J. Egger,et al. GAN-based generation of realistic 3D data: A systematic review and taxonomy , 2022, ArXiv.
[28] Ping Luo,et al. AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation , 2022, NeurIPS.
[29] Bjoern H Menze,et al. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset , 2022, Scientific data.
[30] C. Grana,et al. Improving Segmentation of the Inferior Alveolar Nerve through Deep Label Propagation , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Benjamin Planche,et al. SMPL-A: Modeling Person-Specific Deformable Anatomy , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] John L. Burns,et al. AI recognition of patient race in medical imaging: a modelling study , 2022, The Lancet. Digital health.
[33] Michael J. Black,et al. OSSO: Obtaining Skeletal Shape from Outside , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] A. Skalski,et al. Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling , 2022, Comput. Methods Programs Biomed..
[35] J. Egger,et al. Geometric modeling of aortic dissections through convolution surfaces , 2022, Medical Imaging.
[36] E. Garza-Villarreal,et al. The Mexican magnetic resonance imaging dataset of patients with cocaine use disorder: SUDMEX CONN , 2022, Scientific data.
[37] G. Antoniou,et al. A Statistical Shape Model of the Morphological Variation of the Infrarenal Abdominal Aortic Aneurysm Neck , 2022, Journal of clinical medicine.
[38] S. Ooi,et al. Automated segmentation of normal and diseased coronary arteries - The ASOCA challenge , 2022, Comput. Medical Imaging Graph..
[39] N. Mitra,et al. ShapeFormer: Transformer-based Shape Completion via Sparse Representation , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Maria G. Baldeon Calisto,et al. CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation , 2022, Medical Image Anal..
[41] J. Egger,et al. AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks , 2022, Data in brief.
[42] I. Solaroglu,et al. Tumor Cell Infiltration into the Brain in Glioblastoma: From Mechanisms to Clinical Perspectives , 2022, Cancers.
[43] C. Wachinger,et al. Hippocampal representations for deep learning on Alzheimer’s disease , 2021, Scientific Reports.
[44] Dieter Schmalstieg,et al. Inside-Out Instrument Tracking for Surgical Navigation in Augmented Reality , 2021, VRST.
[45] John O. Prior,et al. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge , 2021, Medical Image Anal..
[46] R. Stiefelhagen,et al. Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation , 2021, AAAI.
[47] M. Koudstaal,et al. The 3D skull 0–4 years: A validated, generative, statistical shape model , 2021, Bone reports.
[48] Kaleem Siddiqi,et al. Medial Spectral Coordinates for 3D Shape Analysis , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[49] J. Sonke,et al. Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Mussarat Yasmin,et al. Brain tumor detection and classification using machine learning: a comprehensive survey , 2021, Complex & Intelligent Systems.
[51] J. Egger,et al. MUG500+: Database of 500 high-resolution healthy human skulls and 29 craniotomy skulls and implants , 2021, Data in brief.
[52] Jie Yang,et al. Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[53] Essa Yacoub,et al. The Human Connectome Project: A retrospective , 2021, NeuroImage.
[54] Marcelo H Ang,et al. Voxel-based Network for Shape Completion by Leveraging Edge Generation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[55] Jiwen Lu,et al. PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[56] Yizhou Yu,et al. ME-PCN: Point Completion Conditioned on Mask Emptiness , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[57] Adam Herout,et al. Deep learning for cranioplasty in clinical practice: Going from synthetic to real patient data , 2021, Comput. Biol. Medicine.
[58] Fabian Prasser,et al. Privacy-preserving data sharing infrastructures for medical research: systematization and comparison , 2021, BMC Medical Informatics and Decision Making.
[59] Nikolaos V. Tsekos,et al. Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge , 2021, Medical Image Anal..
[60] S. Ourselin,et al. Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm , 2021, Scientific Data.
[61] B. Preim,et al. Facial Feature Removal for Anonymization of Neurological Image Data , 2021, Current Directions in Biomedical Engineering.
[62] Dieter Schmalstieg,et al. Automatic skull defect restoration and cranial implant generation for cranioplasty , 2021, Medical Image Anal..
[63] Christos Davatzikos,et al. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification , 2021, ArXiv.
[64] Russell A. Poldrack,et al. The spectrum of data sharing policies in neuroimaging data repositories , 2021, Human brain mapping.
[65] Dieter Schmalstieg,et al. AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design , 2021, IEEE Transactions on Medical Imaging.
[66] Adam P. Harrison,et al. Deep Implicit Statistical Shape Models for 3D Medical Image Delineation , 2021, AAAI.
[67] Thomas Baum,et al. A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data , 2021, Scientific Data.
[68] João Pedrosa,et al. LNDb challenge on automatic lung cancer patient management , 2021, Medical Image Anal..
[69] Sasank Chilamkurthy,et al. SkullBreak / SkullFix – Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks , 2021, Data in brief.
[70] S. Strother,et al. Multisite Comparison of MRI Defacing Software Across Multiple Cohorts , 2021, Frontiers in Psychiatry.
[71] T. Bortfeld,et al. Cross-Modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization , 2021, MICCAI.
[72] Kerem Bölek,et al. The effectiveness of the use of augmented reality in anatomy education: a systematic review and meta-analysis , 2021, Scientific Reports.
[73] Rainer Stiefelhagen,et al. Adaptiope: A Modern Benchmark for Unsupervised Domain Adaptation , 2021, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[74] Jing Xiao,et al. 3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[75] Jens Petersen,et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.
[76] J. Egger,et al. Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact , 2020, PeerJ Comput. Sci..
[77] D. Rubin,et al. CT-ORG, a new dataset for multiple organ segmentation in computed tomography , 2020, Scientific Data.
[78] Daniel L. Rubin,et al. CT-ORG, a new dataset for multiple organ segmentation in computed tomography , 2020, Scientific Data.
[79] R. Frayne,et al. Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations , 2020, Frontiers in Neuroscience.
[80] Congcong Wang,et al. AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem? , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[81] J. Egger,et al. Medical Deep Learning - A systematic Meta-Review , 2020, ArXiv.
[82] Enzo Ferrante,et al. Cranial Implant Design via Virtual Craniectomy with Shape Priors , 2020, AutoImplant@MICCAI.
[83] Mikhail Belyaev,et al. First U-Net Layers Contain More Domain Specific Information Than The Last Ones , 2020, DART/DCL@MICCAI.
[84] Enzo Ferrante,et al. Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy , 2020, MICCAI.
[85] Thomas Baum,et al. A Vertebral Segmentation Dataset with Fracture Grading , 2020, Radiology. Artificial intelligence.
[86] Stella X. Yu,et al. 3D Shape Reconstruction from Free-Hand Sketches , 2020, ECCV Workshops.
[87] Matthias Zwicker,et al. Fine-Grained 3D Shape Classification With Hierarchical Part-View Attention , 2020, IEEE Transactions on Image Processing.
[88] V. Agnese,et al. Statistical Shape Analysis of Ascending Thoracic Aortic Aneurysm: Correlation between Shape and Biomechanical Descriptors , 2020, Journal of personalized medicine.
[89] Yohannes Kassahun,et al. A2D2: Audi Autonomous Driving Dataset , 2020, ArXiv.
[90] Alain Lalande,et al. Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI , 2020, Data.
[91] Jan S. Kirschke,et al. Labeling Vertebrae with Two-dimensional Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy. , 2020, Radiology. Artificial intelligence.
[92] Nadya Shusharina,et al. Automated delineation of the clinical target volume using anatomically constrained 3D expansion of the gross tumor volume. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[93] Tao Tan,et al. Automatic corneal nerve fiber segmentation and geometric biomarker quantification , 2020 .
[94] Nicol'as P'erez de Olaguer,et al. VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images , 2020, Medical Image Anal..
[95] Yaozong Gao,et al. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge , 2019, Medical Image Anal..
[96] Christina Gsaxner,et al. Facial model collection for medical augmented reality in oncologic cranio-maxillofacial surgery , 2019, Scientific Data.
[97] A. Campilho,et al. LNDb: A Lung Nodule Database on Computed Tomography , 2019, ArXiv.
[98] Christopher G Schwarz,et al. Identification of Anonymous MRI Research Participants with Face-Recognition Software. , 2019, The New England journal of medicine.
[99] Tanzila Saba,et al. Brain tumor detection using statistical and machine learning method , 2019, Comput. Methods Programs Biomed..
[100] Richard Frayne,et al. Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks , 2019, Artif. Intell. Medicine.
[101] Michael J. Black,et al. Capture, Learning, and Synthesis of 3D Speaking Styles , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[102] Jan Egger,et al. Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional Denoising Autoencoder , 2019, WorldCIST.
[103] S. Reuzé,et al. The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features , 2019, Scientific Reports.
[104] Ronald M. Summers,et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms , 2019, ArXiv.
[105] Hao Chen,et al. The Liver Tumor Segmentation Benchmark (LiTS) , 2019, Medical Image Anal..
[106] Joan Bruna,et al. Deep Geometric Prior for Surface Reconstruction , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[107] D. Buckley. Surfaces , 2018, Adhesives in Manufacturing.
[108] Hans Lamecker,et al. 3D Shape Analysis for Coarctation of the Aorta , 2018, ShapeMI@MICCAI.
[109] Martial Hebert,et al. PCN: Point Completion Network , 2018, 2018 International Conference on 3D Vision (3DV).
[110] Andreas Geiger,et al. Learning 3D Shape Completion from Laser Scan Data with Weak Supervision , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[111] Mert R. Sabuncu,et al. Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[112] R. Steenbakkers,et al. Geometric Image Biomarker Changes of the Parotid Gland Are Associated With Late Xerostomia. , 2017, International journal of radiation oncology, biology, physics.
[113] Wei Wu,et al. Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 , 2017, ArXiv.
[114] 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).
[115] Christos Davatzikos,et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.
[116] C. Meinel,et al. Deep Learning for Medical Image Analysis , 2017, ArXiv.
[117] Richard Frayne,et al. An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement , 2017, NeuroImage.
[118] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[119] Laurens van der Maaten,et al. Submanifold Sparse Convolutional Networks , 2017, ArXiv.
[120] 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).
[121] Chad DeChant,et al. Shape completion enabled robotic grasping , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[122] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[123] G. Biros,et al. Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma. , 2016, Neurosurgery.
[124] Klaus H. Maier-Hein,et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping , 2016, NeuroImage.
[125] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[126] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[127] Jeffrey N. Chiang,et al. Optimized Brain Extraction for Pathological Brains (optiBET) , 2014, PloS one.
[128] Shruti Gujral,et al. Brain Tumor Detection based on Machine Learning Algorithms , 2014 .
[129] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[130] Florian Jung,et al. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge , 2014, Medical Image Anal..
[131] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[132] Jian Chen,et al. Brain extraction using the watershed transform from markers , 2013, Front. Neuroinform..
[133] Yun Tian,et al. Automatic Sex Determination of Skulls Based on a Statistical Shape Model , 2013, Comput. Math. Methods Medicine.
[134] D. Louis Collins,et al. BEaST: Brain extraction based on nonlocal segmentation technique , 2012, NeuroImage.
[135] Paul M. Thompson,et al. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods , 2011, IEEE Transactions on Medical Imaging.
[136] Arno Klein,et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.
[137] Luc Van Gool,et al. A 3-D Audio-Visual Corpus of Affective Communication , 2010, IEEE Transactions on Multimedia.
[138] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[139] Hans-Peter Meinzer,et al. Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..
[140] Dragomir Anguelov,et al. SCAPE: shape completion and animation of people , 2005, ACM Trans. Graph..
[141] William M. Wells,et al. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.
[142] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[143] Anders M. Dale,et al. A hybrid approach to the Skull Stripping problem in MRI , 2001, NeuroImage.
[144] R. Leahy,et al. Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.
[145] Jyrki Lötjönen,et al. Automatic Reconstruction of 3D Geometry Using Projections and a Geometric Prior Model , 1999, MICCAI.
[146] William E. Lorensen,et al. Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.
[147] Shan Yang,et al. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images , 2022, ArXiv.
[148] Hui Liu,et al. A Geometry-Constrained Deformable Attention Network for Aortic Segmentation , 2022, MICCAI.
[149] S. Durrleman,et al. Progression Models for Imaging Data with Longitudinal Variational Auto Encoders , 2022, MICCAI.
[150] John O. Prior,et al. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT , 2022, HECKTOR@MICCAI.
[151] Mattia Di Bartolomeo,et al. Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes , 2022, IEEE Access.
[152] Costantino Grana,et al. A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal , 2021, VISIGRAPP.
[153] Stefan Zachow,et al. Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks , 2020, AutoImplant@MICCAI.
[154] A. Lundervold,et al. 2D and 3D U-Nets for skull stripping in a large and heterogeneous set of head MRI using fastai , 2020 .
[155] Adam Herout,et al. Cranial Defect Reconstruction Using Cascaded CNN with Alignment , 2020, AutoImplant@MICCAI.
[156] 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.
[157] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .