  As lung cancer evolves, the presence of enlarged and potentially malignant
lymph nodes must be assessed to properly estimate disease progression and
select the best treatment strategy. Following the clinical guidelines,
estimation of short-axis diameter and mediastinum station are paramount for
correct diagnosis. A method for accurate and automatic segmentation is hence
decisive for quantitatively describing lymph nodes. In this study, the use of
3D convolutional neural networks, either through slab-wise schemes or the
leveraging of downsampled entire volumes, is investigated. Furthermore, the
potential impact from simple ensemble strategies is considered. As lymph nodes
have similar attenuation values to nearby anatomical structures, we suggest
using the knowledge of other organs as prior information to guide the
segmentation task. To assess the segmentation and instance detection
performances, a 5-fold cross-validation strategy was followed over a dataset of
120 contrast-enhanced CT volumes. For the 1178 lymph nodes with a short-axis
diameter $\geq10$ mm, our best performing approach reached a patient-wise
recall of 92%, a false positive per patient ratio of 5, and a segmentation
overlap of 80.5%. The method performs similarly well across all stations.
Fusing a slab-wise and a full volume approach within an ensemble scheme
generated the best performances. The anatomical priors guiding strategy is
promising, yet a larger set than four organs appears needed to generate an
optimal benefit. A larger dataset is also mandatory, given the wide range of
expressions a lymph node can exhibit (i.e., shape, location, and attenuation),
and contrast uptake variations.
