Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans

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Rosenbaum, S.J., Lind, T., Antoch, G., Bockisch, A.: False-positive fdg pet uptakethe role of pet/ct. European Radiology 16
Original
2005
Earlier work this paper cites.
Nakajo, M., Jinnouchi, S., Fukukura, Y., Tanabe, H., Tateno, R., Nakajo, M.: The efficacy of whole-body fdg-pet or pet/ct for autoimmune pancreatitis and associated extrapancreatic autoimmune lesions. European Journal of Nuclear Medicine & Molecular Imaging 34
2007
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. pp. 234–241. Springer International Publishing, Cham (2015)
2015
Cited alongside, same era.
Li, H., Jiang, H., Li, S., Wang, M., Wang, Y.: Densex-net: An end-to-end model for lymphoma segmentation in whole-body pet/ct images. IEEE Access PP
2019
Cited alongside, same era.

Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18
2021
Later among the works it cites.
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 574–584 (2022)
2022
Closest in time.