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In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided pseudo-label refinement.
Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe
Hammers, A. et al · 2003
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Local setup errors in image-guided radiotherapy for head and neck cancer patients immobilized with a custom-made device
Giske, K. et al · 2011
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3d slicer: a platform for subject-specific image analysis, visualization, and clinical support
Kikinis, R., Pieper, S. D. & Vosburgh, K. G · 2013
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The cancer imaging archive (tcia): maintaining and operating a public information repository
Clark, K. et al · 2013
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P. & Brox, T · 2015
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Landman, B. et al · 2015
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3d u-net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F., Navab, N. & Ahmadi, S.-A · 2016
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Analyzing human decisions in igrt of head-and-neck cancer patients to teach image registration algorithms what experts know
Stoiber, E. M. et al · 2017
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Head-and-neck squamous cell carcinoma patients with ct taken during pre-treatment, mid-treatment, and post-treatment dataset
Bejarano, T., De Ornelas Couto, M. & Mihaylov, I. B · 2018
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Simpson, A. L. et al · 2019
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Deep learning with mixed supervision for brain tumor segmentation
Mlynarski, P., Delingette, H., Criminisi, A. & Ayache, N · 2019
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Longitudinal fan-beam computed tomography dataset for head-and-neck squamous cell carcinoma patients
Bejarano, T., De Ornelas-Couto, M. & Mihaylov, I. B · 2019
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Self-training with noisy student improves imagenet classification
Xie, Q., Luong, M.-T., Hovy, E. & Le, Q. V · 2020
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Segthor: Segmentation of thoracic organs at risk in ct images
Lambert, Z., Petitjean, C., Dubray, B. & Kuan, S · 2020
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Deep-learning-assisted detection and segmentation of rib fractures from ct scans: Development and validation of fracnet
Jin, L. et al · 2020
Cited alongside, same era.
Early-learning regularization prevents memorization of noisy labels
Liu, S., Niles-Weed, J., Razavian, N. & Fernandez-Granda, C · 2020
Cited alongside, same era.
Multi-modal co-learning for liver lesion segmentation on pet-ct images
Xue, Z. et al · 2021
Cited alongside, same era.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J. & Maier-Hein, K. H · 2021
Cited alongside, same era.
Verse: A vertebrae labelling and segmentation benchmark for multi-detector ct images
Scaling vision transformers
Zhai, X., Kolesnikov, A., Houlsby, N. & Beyer, L · 2022
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Omni-detr: Omni-supervised object detection with transformers
Wang, P. et al · 2022
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One weird trick to improve your semi-weakly supervised semantic segmentation model
Bae, W., Noh, J., Asadabadi, M. J. & Sutherland, D. J · 2022
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A whole-body fdg-pet/ct dataset with manually annotated tumor lesions
Gatidis, S. et al · 2022
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Pediatric chest-abdomen-pelvis and abdomen-pelvis ct images with expert organ contours
Jordan, P. et al · 2022
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Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
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Sekuboyina, A. et al · 2021
Cited alongside, same era.
Ribseg dataset and strong point cloud baselines for rib segmentation from ct scans
Yang, J., Gu, S., Wei, D., Pfister, H. & Ni, B · 2021
Cited alongside, same era.
Deep learning to segment pelvic bones: large-scale ct datasets and baseline models
Liu, P. et al · 2021
Cited alongside, same era.
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
Ma, J. et al · 2021
Cited alongside, same era.
Fully automated body composition analysis in routine ct imaging using 3d semantic segmentation convolutional neural networks
Koitka, S., Kroll, L., Malamutmann, E., Oezcelik, A. & Nensa, F · 2021
Cited alongside, same era.
Right atrium size in the general population
Keller, K. et al · 2021
Cited alongside, same era.
The medical segmentation decathlon
Antonelli, M. et al · 2022
Cited alongside, same era.
Ji, Y. et al · 2022
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Pulmonary artery segmentation challenge 2022, 10.5281/zenodo.6361906 (2022)
Wang, K. et al · 2022
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Unetr: Transformers for 3d medical image segmentation
Hatamizadeh, A. et al · 2022
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Zhang, M. et al · 2023
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Kirillov, A. et al · 2023
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Decoupled semantic prototypes enable learning from arbitrary annotation types for semi-weakly segmentation in expert-driven domains
Reiß, S., Seibold, C., Freytag, A., Rodner, E. & Stiefelhagen, R · 2023
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Annotating 8,000 abdominal ct volumes for multi-organ segmentation in three weeks
Qu, C. et al · 2023
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Label-free liver tumor segmentation
Hu, Q. et al · 2023
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Marinov, Z., Reiß, S., Kersting, D., Kleesiek, J. & Stiefelhagen, R · 2023
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