Automatic lymph node segmentation using deep parallel squeeze & excitation and attention Unet

Abstract

Automatic segmentation and lymph node (LN) detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Yet, it is still a difficult task due to the low contrast of LNs and surrounding soft tissues and the variation in nodal size and shape. We designed a location-guided 3D dual network for LN segmentation. A localization module generates Gaussian masks focused on LNs centralized within selected regions of interest (ROI). Our segmentation model incorporated squeeze & excitation (SE) and attention gate (AG) modules into a conventional 3D UNet architecture to boost useful feature utilization and increase usable feature utilization and segmentation accuracy. Lastly, we provide a simple boundary refinement module to polish the outcomes. We assessed the location-guided LN segmentation network’s performance on a clinical dataset with head and neck cancer. The location-guided network outperformed a comparable architecture without the Gaussian mask in terms of performance.