  Deep learning has revolutionized the accurate segmentation of diseases in
medical imaging. However, achieving such results requires training with
numerous manual voxel annotations. This requirement presents a challenge for
whole-body Positron Emission Tomography (PET) imaging, where lesions are
scattered throughout the body. To tackle this problem, we introduce SW-FastEdit
- an interactive segmentation framework that accelerates the labeling by
utilizing only a few user clicks instead of voxelwise annotations. While prior
interactive models crop or resize PET volumes due to memory constraints, we use
the complete volume with our sliding window-based interactive scheme. Our model
outperforms existing non-sliding window interactive models on the AutoPET
dataset and generalizes to the previously unseen HECKTOR dataset. A user study
revealed that annotators achieve high-quality predictions with only 10 click
iterations and a low perceived NASA-TLX workload. Our framework is implemented
using MONAI Label and is available:
https://github.com/matt3o/AutoPET2-Submission/
