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/
@article{arxiv.2311.14482,
title = {Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images},
author = {Matthias Hadlich and Zdravko Marinov and Moon Kim and Enrico Nasca and Jens Kleesiek and Rainer Stiefelhagen},
journal= {arXiv preprint arXiv:2311.14482},
year = {2023}
}