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.
Adrian Barbu Michael Suehling Xun Xu David Liu S.Kevin Zhou Dorin Comaniciu2
Statistics Department, Florida State Univ., Tallahassee, FL 32306, USA Siemens Corporate Research, Princeton, NJ 08540, USA
国际会议
北京
英文
28-36
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）