Automatic Detection and Segmentation of Axillary Lymph Nodes

Lymph node detection and measurement is a difficult and important part of cancer treatment. In this paper we present a robust and effective learning-based method for the automatic detection of solid lymph nodes from Computed Tomography data. The contributions of the paper are the following. First, it presents a learning based approach to lymph node detection based on Marginal Space Learning. Second, it presents an efficient MRF-based segmentation method for solid lymph nodes. Third, it presents two new sets of features, one set self-aligning to the local gradients and another set based on the segmentation result. An extensive evaluation on 101 volumes containing 362 lymph nodes shows that this method obtains a 82.3% detection rate at 1 false positive per volume, with an average running time of 5-20 seconds per volume.

• Type: Journal
• Year: 2010
• Where: MICCAI
• Authors: Adrian Barbu, Michael Sühling, Xun Xu, David Liu, Shaohua Kevin Zhou, Dorin Comaniciu
• Keywords: Detection, Lymph, Medical Imaging, MICCAI 2010, Solid Lymph