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Deep learning has revolutionized the accurate segmentation of diseases inmedical imaging. However, achieving such results requires training withnumerous manual voxel annotations. This requirement presents a challenge forwhole-body Positron Emission Tomography (PET) imaging, where lesions arescattered throughout the body. To tackle this problem, we introduce SW-FastEdit- an interactive segmentation framework that accelerates the labeling byutilizing only a few user clicks instead of voxelwise annotations. While priorinteractive models crop or resize PET volumes due to memory constraints, we usethe complete volume with our sliding window-based interactive scheme. Our modeloutperforms existing non-sliding window interactive models on the AutoPETdataset and generalizes to the previously unseen HECKTOR dataset. A user studyrevealed that annotators achieve high-quality predictions with only 10 clickiterations and a low perceived NASA-TLX workload. Our framework is implementedusing MONAI Label and is available:https://github.com/matt3o/AutoPET2-Submission/

Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images