  Finding, identifying and segmenting suspicious cancer metastasized lymph
nodes from 3D multi-modality imaging is a clinical task of paramount
importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor
Volume (GTVLN). Determining and delineating the spread of GTVLN is essential in
defining the corresponding resection and irradiating regions for the downstream
workflows of surgical resection and radiotherapy of various cancers. In this
work, we propose an effective distance-based gating approach to simulate and
simplify the high-level reasoning protocols conducted by radiation oncologists,
in a divide-and-conquer manner. GTVLN is divided into two subgroups of
tumor-proximal and tumor-distal, respectively, by means of binary or soft
distance gating. This is motivated by the observation that each category can
have distinct though overlapping distributions of appearance, size and other LN
characteristics. A novel multi-branch detection-by-segmentation network is
trained with each branch specializing on learning one GTVLN category features,
and outputs from multi-branch are fused in inference. The proposed method is
evaluated on an in-house dataset of $141$ esophageal cancer patients with both
PET and CT imaging modalities. Our results validate significant improvements on
the mean recall from $72.5\%$ to $78.2\%$, as compared to previous
state-of-the-art work. The highest achieved GTVLN recall of $82.5\%$ at $20\%$
precision is clinically relevant and valuable since human observers tend to
have low sensitivity (around $80\%$ for the most experienced radiation
oncologists, as reported by literature).
