Abstract
We present a novel two-staged method that employs various 2D-based techniques to deal with the 3D segmentation task. In most of the previous challenges, it is unlikely for 2D CNNs to be comparable with other 3D CNNs since 2D models can hardly capture temporal information. In light of that, we propose using the recent state-of-the-art technique in video object segmentation, combining it with other semi-supervised training techniques to leverage the extensive unlabeled data. Moreover, we introduce a way to generate pseudo-labeled data that is both plausible and consistent for further retraining by using uncertainty estimation. Our code is publicly available at Github.
M.-K. Pham and T.-L. Nguyen-Ho—Equal contribution.
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Acknowledgements
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number DS2020-42-01.
The authors of this paper declare that the segmentation method they implemented for participation in the FLARE 2022 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers.
Furthermore, no manual intervention has been made in the contribution to the results of the proposed method.
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Pham, MK., Nguyen-Ho, TL., Dao, T.T.P., Nguyen, TC., Tran, MT. (2022). Semi-supervised Organ Segmentation with Mask Propagation Refinement and Uncertainty Estimation for Data Generation. In: Ma, J., Wang, B. (eds) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. FLARE 2022. Lecture Notes in Computer Science, vol 13816. Springer, Cham. https://doi.org/10.1007/978-3-031-23911-3_15
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