  Tumor volume and changes in tumor characteristics over time are important
biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely
used for staging and re-staging of cancer, as the radiolabeled
fluorodeoxyglucose is taken up in regions of high metabolism. Unfortunately,
these regions with high metabolism are not specific to tumors and can also
represent physiological uptake by normal functioning organs, inflammation, or
infection, making detailed and reliable tumor segmentation in these scans a
demanding task. This gap in research is addressed by the AutoPET challenge,
which provides a public data set with FDG-PET/CT scans from 900 patients to
encourage further improvement in this field. Our contribution to this challenge
is an ensemble of two state-of-the-art segmentation models, the nn-Unet and the
Swin UNETR, augmented by a maximum intensity projection classifier that acts
like a gating mechanism. If it predicts the existence of lesions, both
segmentations are combined by a late fusion approach. Our solution achieves a
Dice score of 72.12\% on patients diagnosed with lung cancer, melanoma, and
lymphoma in our cross-validation. Code:
https://github.com/heiligerl/autopet_submission
