  Tumor lesion segmentation is one of the most important tasks in medical image
analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission
Tomography~(FDG-PET) is a widely used technique to identify and quantify
metabolically active tumors. However, since FDG-PET scans only provide
metabolic information, healthy tissue or benign disease with irregular glucose
consumption may be mistaken for cancer. To handle this challenge, PET is
commonly combined with Computed Tomography~(CT), with the CT used to obtain the
anatomic structure of the patient. The combination of PET-based metabolic and
CT-based anatomic information can contribute to better tumor segmentation
results. %Computed tomography~(CT) is a popular modality to illustrate the
anatomic structure of the patient. The combination of PET and CT is promising
to handle this challenge by utilizing metabolic and anatomic information. In
this paper, we explore the potential of U-Net for lesion segmentation in
whole-body FDG-PET/CT scans from three aspects, including network architecture,
data preprocessing, and data augmentation. The experimental results demonstrate
that the vanilla U-Net with proper input shape can achieve satisfactory
performance. Specifically, our method achieves first place in both preliminary
and final leaderboards of the autoPET 2022 challenge. Our code is available at
https://github.com/Yejin0111/autoPET2022_Blackbean.
