| Regressing heatmaps for multiple landmark localization using CNNs C Payer, D Štern, H Bischof, M Urschler International conference on medical image computing and computer-assisted …, 2016 | 315 | 2016 |
| Integrating spatial configuration into heatmap regression based CNNs for landmark localization C Payer, D Štern, H Bischof, M Urschler Medical image analysis 54, 207-219, 2019 | 292 | 2019 |
| Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge X Zhuang, L Li, C Payer, D Štern, M Urschler, MP Heinrich, J Oster, ... Medical image analysis 58, 101537, 2019 | 270 | 2019 |
| VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images A Sekuboyina, ME Husseini, A Bayat, M Löffler, H Liebl, H Li, G Tetteh, ... Medical image analysis 73, 102166, 2021 | 214 | 2021 |
| Multi-label whole heart segmentation using CNNs and anatomical label configurations C Payer, D Štern, H Bischof, M Urschler International Workshop on Statistical Atlases and Computational Models of …, 2017 | 187 | 2017 |
| A framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs K Gillette, MAF Gsell, AJ Prassl, E Karabelas, U Reiter, G Reiter, ... Medical image analysis 71, 102080, 2021 | 119 | 2021 |
| Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge J Ma, Y Zhang, S Gu, X An, Z Wang, C Ge, C Wang, F Zhang, Y Wang, ... Medical Image Analysis 82, 102616, 2022 | 117 | 2022 |
| Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net. C Payer, D Stern, H Bischof, M Urschler VISIGRAPP (5: VISAPP), 124-133, 2020 | 90 | 2020 |
| Instance segmentation and tracking with cosine embeddings and recurrent hourglass networks C Payer, D Štern, T Neff, H Bischof, M Urschler International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 89 | 2018 |
| Automatic age estimation and majority age classification from multi-factorial MRI data D Štern, C Payer, N Giuliani, M Urschler IEEE journal of biomedical and health informatics 23 (4), 1392-1403, 2018 | 73 | 2018 |
| Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks C Payer, D Štern, M Feiner, H Bischof, M Urschler Medical image analysis 57, 106-119, 2019 | 72 | 2019 |
| Automated age estimation from MRI volumes of the hand D Štern, C Payer, M Urschler Medical image analysis 58, 101538, 2019 | 65 | 2019 |
| Generative adversarial network based synthesis for supervised medical image segmentation T Neff, C Payer, D Stern, M Urschler Proc. OAGM and ARW joint Workshop 3, 4, 2017 | 56 | 2017 |
| Automated age estimation from hand MRI volumes using deep learning D Štern, C Payer, V Lepetit, M Urschler International conference on medical image computing and computer-assisted …, 2016 | 47 | 2016 |
| Automated integer programming based separation of arteries and veins from thoracic CT images C Payer, M Pienn, Z Bálint, A Shekhovtsov, E Talakic, E Nagy, ... Medical image analysis 34, 109-122, 2016 | 41 | 2016 |
| International workshop on statistical atlases and computational models of the heart C Payer, D Stern, H Bischof, M Urschler Springer, Cham,, 2017 | 40 | 2017 |
| Verse: a vertebrae labelling and segmentation benchmark A Sekuboyina, A Bayat, ME Husseini, M Löffler, M Rempfler, J Kukačka, ... | 33 | 2020 |
| Matwo-capsnet: a multi-label semantic segmentation capsules network S Bonheur, D Štern, C Payer, M Pienn, H Olschewski, M Urschler Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 33 | 2019 |
| Quantitative CT‐derived vessel metrics in idiopathic pulmonary fibrosis: A structure–function study J Jacob, M Pienn, C Payer, M Urschler, M Kokosi, A Devaraj, AU Wells, ... Respirology 24 (5), 445-452, 2019 | 27 | 2019 |
| Multi-factorial age estimation from skeletal and dental MRI volumes D Štern, P Kainz, C Payer, M Urschler International workshop on machine learning in medical imaging, 61-69, 2017 | 27 | 2017 |
Martin UrschlerMedical University Graz在 medunigraz.at 的电子邮件经过验证
