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