Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body
  PET Images
Matthias Hadlich Zdravko Marinov Moon Kim Enrico Nasca Jens Kleesiek Rainer Stiefelhagen
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/