Abstract
Recently, the nnU-Net network had achieved excellent performance in many medical image segmentation tasks. However, it also had some obvious problems, such as being able to only perform fully supervised tasks, excessive resource consumption in the predict. Therefore, in the abdominal multi-organ challenge of FLARE23, only incomplete labeled data was provided, and the size of them was too large, which made the original nnU-Net difficult to run. Based on this, we had designed a framework that utilized generated pseudo labels and two-stage segmentation for fast and effective prediction. Specifically, we designed three nnU-Net, one for generating high-quality pseudo labels for unlabeled data, the other for generating coarse segmentation to guide cropping, and the third for achieving effective segmentation. Our method achieved an average DSC score of 88.87% and 38.00% for the organs and lesions on the validation set and the average running time and area under GPU memory-time cure are 45 s and 3000 MB, respectively.
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Acknowledgements
The authors of this paper declare that the segmentation method they implemented for participation in the FLARE 2023 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. The proposed solution is fully automatic without any manual intervention. We thank all the data owners for making the CT scans publicly available and CodaLab [15] for hosting the challenge platform.
This work was supported by National Natural Science Foundation of China (62271149), Fujian Provincial Natural Science Foundation project (2021J02019, 2021J01578).
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Yang, X., Zhang, X., Yan, X., Ding, W., Chen, H., Huang, L. (2024). Abdomen Multi-organ Segmentation Using Pseudo Labels and Two-Stage. In: Ma, J., Wang, B. (eds) Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT. FLARE 2023. Lecture Notes in Computer Science, vol 14544. Springer, Cham. https://doi.org/10.1007/978-3-031-58776-4_4
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