Abstract
Abdominal CT organ segmentation is known to be challenging. The segmentation of multiple abdominal organs enables quantitative analysis of different organs, providing invaluable input for computer-aided diagnosis (CAD) systems. Based on nnUNet, we develop an abdominal organ segmentation method applicable to both abdominal CT and whole-body CT data. The proposed new training pipeline combines the Kullback-Leibler semi-supervised learning and fully supervised learning, and employs a coarse to fine strategy and GPU accelerated interpolation. Our method achieves a mean Dice Similarity Coefficient (DSC) of 0.873/0.870 and a Normalized Surface Dice (NSD) of 0.911/0.915 on the FLARE 2022 validation/test dataset, with an average process time of 12.27 s per case. Overall, we ranked the fifth place in the FLARE 2022 Challenge. The code is available at https://github.com/Solor-pikachu/Infer-MedSeg-With-Low-Resource.
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Acknowledgements
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. The proposed solution is fully automatic without any manual intervention. We thank to the timely help given by Bingding Huang in supporting GPU machine, Sixin Liu in supporting word spelling and grammar correction. This study is supported in part by Natural Science Foundation of Top Talent of Shenzhen Technology University (Grants No. 20200208 to Lyu, Mengye and No. GDRC202134 to Li, Jingyu), and the National Natural Science Foundation of China (Grant No. 62101348 to Lyu, Mengye).
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Huang, S. et al. (2022). Abdominal CT Organ Segmentation by Accelerated nnUNet with a Coarse to Fine Strategy. 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_3
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DOI: https://doi.org/10.1007/978-3-031-23911-3_3
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