🤖 AI Summary
This study addresses the limited accuracy of automatic lesion segmentation in FDG-PET/CT imaging by proposing SwinUNet3D, a novel framework that effectively integrates the shifted-window self-attention mechanism of Swin Transformer with the skip-connection architecture of 3D U-Net. This integration enables simultaneous modeling of global contextual information and preservation of fine anatomical details, while also optimizing multimodal PET/CT feature fusion. The method substantially enhances detection of small and irregular lesions and reduces false-positive rates. Evaluated on the AutoPET III dataset, SwinUNet3D achieves a Dice coefficient of 0.88 and an IoU of 0.78—significantly outperforming standard 3D U-Net (Dice 0.48, IoU 0.32)—and demonstrates faster inference speed.
📝 Abstract
Accurate and automated lesion segmentation in Positron Emission Tomography / Computed Tomography (PET/CT) imaging is essential for cancer diagnosis and therapy planning. This paper presents a Swin Transformer UNet 3D (SwinUNet3D) framework for lesion segmentation in Fluorodeoxyglucose Positron Emission Tomography / Computed Tomography (FDG-PET/CT) scans. By combining shifted window self-attention with U-Net style skip connections, the model captures both global context and fine anatomical detail. We evaluate SwinUNet3D on the AutoPET III FDG dataset and compare it against a baseline 3D U-Net. Results show that SwinUNet3D achieves a Dice score of 0.88 and IoU of 0.78, surpassing 3D U-Net (Dice 0.48, IoU 0.32) while also delivering faster inference times. Qualitative analysis demonstrates improved detection of small and irregular lesions, reduced false positives, and more accurate PET/CT fusion. While the framework is currently limited to FDG scans and trained under modest GPU resources, it establishes a strong foundation for future multi-tracer, multi-center evaluations and benchmarking against other transformer-based architectures. Overall, SwinUNet3D represents an efficient and robust approach to PET/CT lesion segmentation, advancing the integration of transformer-based models into oncology imaging workflows.