Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station
  Stratification
Ke Yan Dakai Jin Dazhou Guo Minfeng Xu Na Shen Xian-Sheng Hua Xianghua Ye Le Lu
Finding abnormal lymph nodes in radiological images is highly important for
various medical tasks such as cancer metastasis staging and radiotherapy
planning. Lymph nodes (LNs) are small glands scattered throughout the body.
They are grouped or defined to various LN stations according to their
anatomical locations. The CT imaging appearance and context of LNs in different
stations vary significantly, posing challenges for automated detection,
especially for pathological LNs. Motivated by this observation, we propose a
novel end-to-end framework to improve LN detection performance by leveraging
their station information. We design a multi-head detector and make each head
focus on differentiating the LN and non-LN structures of certain stations.
Pseudo station labels are generated by an LN station classifier as a form of
multi-task learning during training, so we do not need another explicit LN
station prediction model during inference. Our algorithm is evaluated on 82
patients with lung cancer and 91 patients with esophageal cancer. The proposed
implicit station stratification method improves the detection sensitivity of
thoracic lymph nodes from 65.1% to 71.4% and from 80.3% to 85.5% at 2 false
positives per patient on the two datasets, respectively, which significantly
outperforms various existing state-of-the-art baseline techniques such as
nnUNet, nnDetection and LENS.