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Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body.
Fully automated, semantic segmentation of whole-body 18f-fdg pet/ct images based on data-centric artificial intelligence
Sundar, L. K. S., Yu, J., Muzik, O., Kulterer, O. C., Fueger, B., Kifjak, D., Nakuz, T., Shin, H. M., Sima, A. K., Kitzmantl, D., et al. (2022) · 1948
Earlier work this paper cites.
Quantitative mri for the assessment of bone structure and function
Wehrli, F. W., Song, H. K., Saha, P. K., and Wright, A. C. (2006) · 2006
Earlier work this paper cites.
Musculoskeletal mri
Helms, C. A. (2009) · 2009
Earlier work this paper cites.
An image-based approach to understanding the physics of mr artifacts
Morelli, J. N., Runge, V. M., Ai, F., Attenberger, U., Vu, L., Schmeets, S. H., Nitz, W. R., and Kirsch, J. E. (2011) · 2011
Earlier work this paper cites.
Advances in magnetic resonance imaging: how they are changing the management of prostate cancer
Sciarra, A., Barentsz, J., Bjartell, A., Eastham, J., Hricak, H., Panebianco, V., and Witjes, J. A. (2011) · 2011
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
Earlier work this paper cites.
V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F., Navab, N., and Ahmadi, S.-A. (2016) · 2016
Earlier work this paper cites.
Comparison of atlas-based techniques for whole-body bone segmentation
Arabi, H. and Zaidi, H. (2017) · 2017
Earlier work this paper cites.
Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (mri), using classification forests (cf s), convolutional neural networks (cnn s), and a multi-atlas (ma) approach
Lavdas, I., Glocker, B., Kamnitsas, K., Rueckert, D., Mair, H., Sandhu, A., Taylor, S. A., Aboagye, E. O., and Rockall, A. G. (2017) · 2017
Earlier work this paper cites.
Interactive segmentation in mri for orthopedic surgery planning: bone tissue
Ozdemir, F., Karani, N., Fürnstahl, P., and Goksel, O. (2017) · 2017
Earlier work this paper cites.
Tversky loss function for image segmentation using 3d fully convolutional deep networks
Salehi, S. S. M., Erdogmus, D., and Gholipour, A. (2017) · 2017
Earlier work this paper cites.
Wrist: A wrist image segmentation toolkit for carpal bone delineation from mri
Foster, B., Joshi, A. A., Borgese, M., Abdelhafez, Y., Boutin, R. D., and Chaudhari, A. J. (2018) · 2018
Earlier work this paper cites.
Niftynet: a deep-learning platform for medical imaging
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., Whyntie, T., Nachev, P., Modat, M., Barratt, D. C., Ourselin, S., Cardoso, M. J., and Vercauteren, T. (2018) · 2018
Earlier work this paper cites.
Attention u-net: Learning where to look for the pancreas
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · 2018
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MRI in Practice
Westbrook, C. and Talbot, J. (2018) · 2018
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Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative
Ambellan, F., Tack, A., Ehlke, M., and Zachow, S. (2019) · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
Cited alongside, same era.
Automated segmentation of tissues using ct and mri: a systematic review
Lenchik, L., Heacock, L., Weaver, A. A., Boutin, R. D., Cook, T. S., Itri, J., Filippi, C. G., Gullapalli, R. P., Lee, J., Zagurovskaya, M., et al. (2019) · 2019
Cited alongside, same era.
An overview of deep learning in medical imaging focusing on mri
Lundervold, A. S. and Lundervold, A. (2019) · 2019
Cited alongside, same era.
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on mri
Mazurowski, M. A., Buda, M., Saha, A., and Bashir, M. R. (2019) · 2019
Cited alongside, same era.
Multi-atlas segmentation of the skeleton from whole-body mri—impact of iterative background masking
Ceranka, J., Verga, S., Kvasnytsia, M., Lecouvet, F., Michoux, N., De Mey, J., Raeymaekers, H., Metens, T., Absil, J., and Vandemeulebroucke, J. (2020) · 2020
Cited alongside, same era.
Unetr: Transformers for 3d medical image segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., and Xu, D. (2022) · 2022
Later among the works it cites.
Atlas-based segmentation in extraction of knee joint bone structures from ct and mr
Zarychta, P. (2022) · 2022
Later among the works it cites.
Artificial intelligence in knee osteoarthritis: A comprehensive review
Cigdem, O. and Deniz, C. M. (2023) · 2023
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Continual segment: Towards a single, unified and non-forgetting continual segmentation model of 143 whole-body organs in ct scans
Ji, Z., Guo, D., Wang, P., Yan, K., Lu, L., Xu, M., Wang, Q., Ge, J., Gao, M., Ye, X., et al. (2023) · 2023
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Segment anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., and Girshick, R. (2023) · 2023
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Diagnostic modality in spine disease: a review
Kim, G.-U., Chang, M. C., Kim, T. U., and Lee, G. W. (2020) · 2020
Cited alongside, same era.
Deep learning for classification of bone lesions on routine mri
Eweje, F. R., Bao, B., Wu, J., Dalal, D., Liao, W.-h., He, Y., Luo, Y., Lu, S., Zhang, P., Peng, X., et al. (2021) · 2021
Cited alongside, same era.
Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., and Xu, D. (2021) · 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., and Maier-Hein, K. H. (2021) · 2021
Cited alongside, same era.
3d mri with ct-like bone contrast–an overview of current approaches and practical clinical implementation
Lee, K., Sim, F. Y., et al. (2021) · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021) · 2021
Cited alongside, same era.
U-net and its variants for medical image segmentation: A review of theory and applications
Siddique, N., Paheding, S., Elkin, C. P., and Devabhaktuni, V. (2021) · 2021
Cited alongside, same era.
Sdmt: Spatial dependence multi-task transformer network for 3d knee mri segmentation and landmark localization
Li, X., Lv, S., Li, M., Zhang, J., Jiang, Y., Qin, Y., Luo, H., and Yin, S. (2023) · 2023
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Segment anything in medical images
Ma, J. and Wang, B. (2023) · 2023
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Segment anything model for medical image analysis: An experimental study
Mazurowski, M. A., Dong, H., Gu, H., Yang, J., Konz, N., and Zhang, Y. (2023) · 2023
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Ct-based automatic spine segmentation using patch-based deep learning
Qadri, S. F., Lin, H., Shen, L., Ahmad, M., Qadri, S., Khan, S., Khan, M., Zareen, S. S., Akbar, M. A., Bin Heyat, M. B., et al. (2023) · 2023
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Lumbar spine segmentation in mr images: a dataset and a public benchmark
van der Graaf, J. W., van Hooff, M. L., Buckens, C. F., Rutten, M., van Susante, J. L., Kroeze, R. J., de Kleuver, M., van Ginneken, B., and Lessmann, N. (2023) · 2023
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Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images
Wasserthal, J., Breit, H.-C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., Heye, T., Boll, D. T., Cyriac, J., Yang, S., et al. (2023) · 2023
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Medical sam adapter: Adapting segment anything model for medical image segmentation
Wu, J., Ji, W., Liu, Y., Fu, H., Xu, M., Xu, Y., and Jin, Y. (2023) · 2023
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Femurs segmentation by machine learning from ct scans combined with autonomous finite elements in orthopedic and endocrinology applications
Yosibash, Z., Katz, Y., Nir, T., and Sternheim, A. (2023) · 2023
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Faster segment anything: Towards lightweight sam for mobile applications
Zhang, C., Han, D., Qiao, Y., Kim, J. U., Bae, S.-H., Lee, S., and Hong, C. S. (2023) · 2023
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An effective u-net and bisenet complementary network for spine segmentation
Deng, Y., Gu, F., Zeng, D., Lu, J., Liu, H., Hou, Y., and Zhang, Q. (2024) · 2024
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Segment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., Liu, S., Chi, H., Hu, X., Yue, K., Li, L., Grau, V., Fan, D.-P., Dong, F., and Ni, D. (2024) · 2024
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