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Tumor volume and changes in tumor characteristics over time are importantbiomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinelyused for staging and re-staging of cancer, as the radiolabeledfluorodeoxyglucose is taken up in regions of high metabolism. Unfortunately,these regions with high metabolism are not specific to tumors and can alsorepresent physiological uptake by normal functioning organs, inflammation, orinfection, making detailed and reliable tumor segmentation in these scans ademanding 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 toencourage further improvement in this field. Our contribution to this challengeis an ensemble of two state-of-the-art segmentation models, the nn-Unet and theSwin UNETR, augmented by a maximum intensity projection classifier that actslike a gating mechanism. If it predicts the existence of lesions, bothsegmentations are combined by a late fusion approach. Our solution achieves aDice score of 72.12\% on patients diagnosed with lung cancer, melanoma, andlymphoma in our cross-validation. Code:https://github.com/heiligerl/autopet_submission

AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier