Automatic Lymph Node Cluster Segmentation Using Holistically-Nested Neural Networks and Structured Optimization in CT Images

Lymph node segmentation is an important yet challenging problem in medical image analysis. The presence of enlarged lymph nodes (LNs) signals the onset or progression of a malignant disease or infection. In the thoracoabdominal (TA) body region, neighboring enlarged LNs often spatially collapse into “swollen” lymph node clusters (LNCs) (up to 9 LNs in our dataset). Accurate segmentation of TA LNCs is complexified by the noticeably poor intensity and texture contrast among neighboring LNs and surrounding tissues, and has not been addressed in previous work. This paper presents a novel approach to TA LNC segmentation that combines holistically-nested neural networks (HNNs) and structured optimization (SO). Two HNNs, built upon recent fully convolutional networks (FCNs) and deeply supervised networks (DSNs), are trained to learn the LNC appearance (HNN-A) or contour (HNN-C) probabilistic output maps, respectively. HNN first produces the class label maps with the same resolution as the input image, like FCN. Afterwards, HNN predictions for LNC appearance and contour cues are formulated into the unary and pairwise terms of conditional random fields (CRFs), which are subsequently solved using one of three different SO methods: dense CRF, graph cuts, and boundary neural fields (BNF). BNF yields the highest quantitative results. Its mean Dice coefficient between segmented and ground truth LN volumes is 82.1 % ± 9.6 %, compared to 73.0 % ± 17.6 % for HNN-A alone. The LNC relative volume (\(cm^3\)) difference is 13.7 % ± 13.1 %, a promising result for the development of LN imaging biomarkers based on volumetric measurements.