AutoImplant 2020-first MICCAI challenge on automatic cranial implant design J Li, P Pimentel, A Szengel, M Ehlke, H Lamecker, S Zachow, L Estacio, ... IEEE transactions on medical imaging 40 (9), 2329-2342, 2021 | 42 | 2021 |
Segmentation of head and neck organs at risk using cnn with batch dice loss O Kodym, M Španěl, A Herout Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany …, 2019 | 33 | 2019 |
SkullBreak/SkullFix–Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks O Kodym, J Li, A Pepe, C Gsaxner, S Chilamkurthy, J Egger, M Španěl Data in Brief 35, 106902, 2021 | 23 | 2021 |
Brno mobile OCR dataset M Kišš, M Hradiš, O Kodym 2019 International Conference on Document Analysis and Recognition (ICDAR …, 2019 | 21 | 2019 |
Page layout analysis system for unconstrained historic documents O Kodym, M Hradiš Document Analysis and Recognition–ICDAR 2021: 16th International Conference …, 2021 | 19 | 2021 |
Skull shape reconstruction using cascaded convolutional networks O Kodym, M Španěl, A Herout Computers in Biology and Medicine 123, 103886, 2020 | 19 | 2020 |
Why is the winner the best? M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, S Ali, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 17 | 2023 |
Deep learning for cranioplasty in clinical practice: Going from synthetic to real patient data O Kodym, M Španěl, A Herout Computers in Biology and Medicine 137, 104766, 2021 | 14 | 2021 |
Cranial defect reconstruction using cascaded CNN with alignment O Kodym, M Španěl, A Herout Towards the Automatization of Cranial Implant Design in Cranioplasty: First …, 2020 | 14 | 2020 |
Semi-automatic CT Image Segmentation using Random Forests Learned from Partial Annotations. O Kodym, M Spanel BIOIMAGING 740, 124-131, 2018 | 13 | 2018 |
Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the autoimplant 2021 cranial implant design challenge J Li, DG Ellis, O Kodym, L Rauschenbach, C Rieß, U Sure, KH Wrede, ... Medical Image Analysis, 102865, 2023 | 11 | 2023 |
: text-guided transformer GAN for restoring document readability and perceived quality O Kodym, M Hradiš International Journal on Document Analysis and Recognition (IJDAR) 25 (1), 15-28, 2022 | 7 | 2022 |
Evaluating Deep Learning Uncertainty Measures in Cephalometric Landmark Localization. D Drevický, O Kodym BIOIMAGING, 213-220, 2020 | 6 | 2020 |
Segmentation of defective skulls from ct data for tissue modelling O Kodym, M Španěl, A Herout arXiv preprint arXiv:1911.08805, 2019 | 4 | 2019 |
Segmentation of Defective Skulls from CT Data for Tissue Modelling O Kodym, M Španěl, A Herout Towards the Automatization of Cranial Implant Design in Cranioplasty II …, 2021 | 3 | 2021 |
AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design A Bayat, Z Liu, M Spanel, M Ehlke, B Wang, O Kodym, H Ramm, DG Ellis, ... Institute of Electrical and Electronics Engineers Inc., 2021 | | 2021 |
Benchmarking medical segmentation models with limited training sets K Trávnıcková, O Kodym | | |
SkullBreak dataset: An open dataset for training and validation of skull reconstruction models O Kodym, M Španel, A Herout | | |
SEGMENTATION OF HIPPOCAMPUS IN MRI DATA O Kodym | | |
Erratum: Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss O Kodym | | |