Abstract
Abdominal multi-organ segmentation is fast becoming a key instrument in preoperative diagnosis. Using the results of abdominal CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for surgical planning. In this paper, we propose a stable three-stage fast automatic segmentation method for abdominal 13 organs: liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum. Our method includes preprocessing the CT data, segmenting the multi-organ and post-processing the segmentation outputs. The results on the test set show that the average DSC performance is about 0.766. The average time and GPU memory consumption for each case is 81.42 s and 1953 MB.
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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. This work was supported by Natural Science Foundation of China (Grant No. 62173014) and Natural Science Foundation of Beijing Municipality (Grant No. L192057).
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Lv, Y., Ning, Y., Wang, J. (2022). Coarse to Fine Automatic Segmentation of Abdominal Multiple Organs. 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_20
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