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/

翻译：深度学习已彻底改变了医学影像中疾病的精确分割。然而，获得此类结果需要使用大量人工体素标注进行训练。这一需求对全身正电子发射断层扫描（PET）成像构成了挑战，因为病灶散布于全身。为解决此问题，我们提出SW-FastEdit——一种交互式分割框架，通过仅利用少量用户点击而非逐体素标注来加速标注过程。由于内存限制，先前的交互式模型会对PET体素进行裁剪或缩放，而我们采用基于滑窗的交互方案处理完整体素。我们的模型在AutoPET数据集上优于现有的非滑窗交互式模型，并能泛化至先前未见过的HECKTOR数据集。一项用户研究表明，标注者仅需10次点击迭代即可获得高质量预测结果，且感知的NASA-TLX工作负荷较低。本框架基于MONAI Label实现，开源地址：https://github.com/matt3o/AutoPET2-Submission/