SDF-Net: A Hybrid Detection Network for Mediastinal Lymph Node Detection on Contrast CT Images
This work addresses a domain-specific problem in medical imaging for cancer diagnosis and staging, with incremental improvements in detection methods.
The paper tackled the problem of detecting mediastinal lymph nodes on contrast CT images, which is challenging due to low contrast and irregular shapes, by proposing SDF-Net, a hybrid network that integrates segmentation and detection features, achieving promising performance.
Accurate lymph node detection and quantification are crucial for cancer diagnosis and staging on contrast-enhanced CT images, as they impact treatment planning and prognosis. However, detecting lymph nodes in the mediastinal area poses challenges due to their low contrast, irregular shapes and dispersed distribution. In this paper, we propose a Swin-Det Fusion Network (SDF-Net) to effectively detect lymph nodes. SDF-Net integrates features from both segmentation and detection to enhance the detection capability of lymph nodes with various shapes and sizes. Specifically, an auto-fusion module is designed to merge the feature maps of segmentation and detection networks at different levels. To facilitate effective learning without mask annotations, we introduce a shape-adaptive Gaussian kernel to represent lymph node in the training stage and provide more anatomical information for effective learning. Comparative results demonstrate promising performance in addressing the complex lymph node detection problem.