A Two-stage Abdominal Lymph Node Detection Algorithm Based on Multi-scale 2.5D Input
Junyi Le, Wenxin Hu, Kun Yu · 2023
Abdominal lymph nodes (ALNs) play a pivotal role in tumor metastasis, necessitating their early detection for tumor diagnosis and treatment decisions. However, due to varying ALN sizes and complex abdominal environment, existing methods face challenges, including small ALN absence, high False Positive (FP) rate and low efficiency. This study aims to propose a two-stage ALN detection approach based on multi-scale 2.5D input to overcome these difficulties. It leverages multi-scale images and the Skip-Attention module to generate ALN cadidate regions in the first stage. Then, a new 2.5D input is constructed by extracting slices from different planes and identifing genuine ALNs via the FP reduction algorithm. We validated our method with the dataset containing both public and real patient data. The first stage achieved an 81.1% detection rate with 21 FP/case, while the second stage reached an AUC value of 0.94 after ensemble learning. The final cascade experiment illustrates that our model achieved a 73.5% detection rate, surpassing other state-of-the-art methods including 3D models by at least a margin of 3.3% and significantly improving time efficiency.