Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34

2014

Earlier work this paper cites.

Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence mri. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 581–588. Springer (2016)

2016

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Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37

2018

Earlier work this paper cites.

Can, Y.B., Chaitanya, K., Mustafa, B., Koch, L.M., Konukoglu, E., Baumgartner, C.F.: Learning to segment medical images with scribble-supervision alone. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. pp. 236–244. Springer (2018)

2018

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Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: Normalized cut loss for weakly-supervised cnn segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1818–1827 (2018)

2018

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Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE transactions on pattern analysis and machine intelligence 41

2018

Earlier work this paper cites.

Ji, Z., Shen, Y., Ma, C., Gao, M.: Scribble-based hierarchical weakly supervised learning for brain tumor segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22. pp. 175–183. Springer (2019)

2019

Earlier work this paper cites.

Lee, H., Jeong, W.K.: Scribble2label: Scribble-supervised cell segmentation via self-generating pseudo-labels with consistency. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23. pp. 14–23. Springer (2020)

2020

Earlier work this paper cites.

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

Earlier work this paper cites.

Luo, X., Liao, W., Xiao, J., Chen, J., Song, T., Zhang, X., Li, K., Metaxas, D.N., Wang, G., Zhang, S.: Word: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from ct image. arXiv preprint arXiv:2111.02403 (2021)

Original

2021

Cited alongside, same era.

Valvano, G., Leo, A., Tsaftaris, S.A.: Learning to segment from scribbles using multi-scale adversarial attention gates. IEEE Transactions on Medical Imaging 40

2021

Cited alongside, same era.

Chen, Q., Hong, Y.: Scribble2d5: Weakly-supervised volumetric image segmentation via scribble annotations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 234–243. Springer (2022)

2022

Cited alongside, same era.

Ji, Y., Bai, H., Ge, C., Yang, J., Zhu, Y., Zhang, R., Li, Z., Zhanng, L., Ma, W., Wan, X., et al.: Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Advances in Neural Information Processing Systems 35

2022

Han, M., Luo, X., Liao, W., Zhang, S., Zhang, S., Wang, G.: Scribble-based 3d multiple abdominal organ segmentation via triple-branch multi-dilated network with pixel-and class-wise consistency. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 33–42. Springer (2023)

2023

Later among the works it cites.

Li, Z., Zheng, Y., Luo, X., Shan, D., Hong, Q.: Scribblevc: Scribble-supervised medical image segmentation with vision-class embedding. In: Proceedings of the 31st ACM International Conference on Multimedia. pp. 3384–3393 (2023)

2023

Later among the works it cites.

Zhang, K., Zhuang, X.: Zscribbleseg: Zen and the art of scribble supervised medical image segmentation. arXiv preprint arXiv:2301.04882 (2023)

Original

2023

Later among the works it cites.

Zhou, M., Xu, Z., Zhou, K., Tong, R.K.y.: Weakly supervised medical image segmentation via superpixel-guided scribble walking and class-wise contrastive regularization. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 137–147. Springer (2023)