Feasibility of End-To-End Trainable Two-Stage U-Net for Detection of Axillary Lymph Nodes in Contrast-Enhanced CT Based Scans on Sparse Annotations

Manual reading of enlarged lymph nodes is time-consuming, error-prone and suffers from inter-observer variability. We propose a mostly generic computer-aided detection system, which can be trained in an end-to-end fashion from sparse annotations, to automatically detect axillary lymph nodes. Our pipeline is a two-stage approach, where both stages are performed using the same U-net architecture: volume of interest localization (axillary region) and then axillary lymph node detection within the VOI. Our dataset comprised 492 lymph nodes (median diameter 7 mm) from 76 patients, and our system achieved an 83% accuracy at 6.7 FP per scan on the test data.