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Automatic segmentation of tumor lesions is a critical initial processing stepfor quantitative PET/CT analysis. However, numerous tumor lesions withdifferent shapes, sizes, and uptake intensity may be distributed in differentanatomical contexts throughout the body, and there is also significant uptakein healthy organs. Therefore, building a systemic PET/CT tumor lesionsegmentation model is a challenging task. In this paper, we propose a novelstep-by-step 3D segmentation method to address this problem. We achieved Dicescore of 0.92, false positive volume of 0.89 and false negative volume of 0.53on preliminary test set.The code of our work is available on the followinglink: https://github.com/rightl/autopet.

AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body FDG-PET/CT