Abdominal CT Organ Segmentation by Accelerated nnUNet with a Coarse to Fine Strategy

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 .