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Finding, identifying and segmenting suspicious cancer metastasized lymphnodes from 3D multi-modality imaging is a clinical task of paramountimportance. In radiotherapy, they are referred to as Lymph Node Gross TumorVolume (GTVLN). Determining and delineating the spread of GTVLN is essential indefining the corresponding resection and irradiating regions for the downstreamworkflows of surgical resection and radiotherapy of various cancers. In thiswork, we propose an effective distance-based gating approach to simulate andsimplify the high-level reasoning protocols conducted by radiation oncologists,in a divide-and-conquer manner. GTVLN is divided into two subgroups oftumor-proximal and tumor-distal, respectively, by means of binary or softdistance gating. This is motivated by the observation that each category canhave distinct though overlapping distributions of appearance, size and other LNcharacteristics. A novel multi-branch detection-by-segmentation network istrained with each branch specializing on learning one GTVLN category features,and outputs from multi-branch are fused in inference. The proposed method isevaluated on an in-house dataset of $141$ esophageal cancer patients with bothPET and CT imaging modalities. Our results validate significant improvements onthe mean recall from $72.5\%$ to $78.2\%$, as compared to previousstate-of-the-art work. The highest achieved GTVLN recall of $82.5\%$ at $20\%$precision is clinically relevant and valuable since human observers tend tohave low sensitivity (around $80\%$ for the most experienced radiationoncologists, as reported by literature).

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy