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

Manual detection of lymph nodes by a radiologist is time-consuming, error-prone and suffers from interobserver variability. We propose a mostly generic computer-aided detection system, which can be trained in an end-to-end fashion, to automatically detect axillary lymph nodes using state of the art fully convolutional neural networks. We aim at a system that can be easily transferred to other body regions such as the mediastinal region. Our pipeline is a two-stage approach, where first a volume of interest (VOI) (axillary region) is localized and then axillary lymph node detection is performed within the VOI. The training was done on 58 CT volumes from 36 patients comprising 300 axillary lymph nodes. On our test dataset, consisting of 75 axillary lymph nodes in the size range 5-10 mm and 17 larger than 10 mm from 30 different patients, we achieved a sensitivity of 83% with 6.7 FPs per volume on average.