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

Abstract

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 (GTV$_{LN}$). Determining and delineating the spread of GTV$_{LN}$ 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. GTV$_{LN}$ 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 GTV$_{LN}$ 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 GTV$_{LN}$ recall of $82.5\%$ at $20\%$ precision is clinically relevant and valuable since human observers tend to have low sensitivity ($\sim$80% for the most experienced radiation oncologists, as reported by literature [5]).