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Recent progress in automated PET/CT lesion segmentation using deep learningmethods has demonstrated the feasibility of this task. However, tumor lesiondetection and segmentation in whole-body PET/CT is still a chal-lenging task.To promote research on machine learning-based automated tumor lesionsegmentation on whole-body FDG-PET/CT data, Automated Lesion Segmentation inWhole-Body FDG-PET/CT (autoPET) challenge is held, and a large, publiclyavailable training dataset is provided. In this report, we present our solutionto the autoPET challenge. We employ multi-modal residual U-Net with deep supervision. The experimental results for five preliminary test cases show that Dicescore is 0.79 +/- 0.21.

Automated Lesion Segmentation in Whole-Body FDG-PET/CT with Multi-modality Deep Neural Networks