  Finding and identifying scatteredly-distributed, small, and critically
important objects in 3D oncology images is very challenging. We focus on the
detection and segmentation of oncology-significant (or suspicious cancer
metastasized) lymph nodes (OSLNs), which has not been studied before as a
computational task. Determining and delineating the spread of OSLNs is
essential in defining the corresponding resection/irradiating regions for the
downstream workflows of surgical resection and radiotherapy of various cancers.
For patients who are treated with radiotherapy, this task is performed by
experienced radiation oncologists that involves high-level reasoning on whether
LNs are metastasized, which is subject to high inter-observer variations. In
this work, we propose a divide-and-conquer decision stratification approach
that divides OSLNs into tumor-proximal and tumor-distal categories. This is
motivated by the observation that each category has its own different
underlying distributions in appearance, size and other characteristics. Two
separate detection-by-segmentation networks are trained per category and fused.
To further reduce false positives (FP), we present a novel global-local network
(GLNet) that combines high-level lesion characteristics with features learned
from localized 3D image patches. Our method is evaluated on a dataset of 141
esophageal cancer patients with PET and CT modalities (the largest to-date).
Our results significantly improve the recall from $45\%$ to $67\%$ at $3$ FPs
per patient as compared to previous state-of-the-art methods. The highest
achieved OSLN recall of $0.828$ is clinically relevant and valuable.
