Lymph Node Detection in 3-D Chest CT using a Spatial Prior Probability

Lymph nodes have high clinical relevance but detection is challenging as they are hard to see due to low contrast and irregular shape. In this paper, a method for fully automatic mediastinal lymph node detection in 3-D computed tomography (CT) images of the chest area is proposed. Discriminative learning is used to detect lymph nodes based on their appearance. Because lymph nodes can easily be confused with other structures, it is vital to incorporate as much anatomical knowledge as possible to achieve good detection rates. Here, a learned prior of the spatial distribution is proposed to model this knowledge. As atlas matching is generally inaccurate in the chest area because of anatomical variations, this prior is not learned in the space of a single atlas, but in the space of multiple ones that are attached to anatomical structures. During test, the priors are weighted and merged according to spatial distances. Cross-validation on 54 CT datasets showed that the prior based detector yields a true positive rate of 52.3% for seven false positives per volume image, which is about two times better than without a spatial prior.