Abstract
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.
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References
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., et al.: Active shape models-their training and application. CVIU 61(1), 38–59 (1995)
Dornheim, J., Seim, H., Preim, B., Hertel, I., Strauss, G.: Segmentation of Neck Lymph Nodes in CT Datasets with Stable 3D Mass-Spring Models Segmentation of Neck Lymph Nodes. Academic Radiology 14(11), 1389–1399 (2007)
Dornheim, L., Dornheim, J.: Automatische Detektion von Lymphknoten in CT-Datensätzen des Halses. In: BVM (2008)
Feuerstein, M., Deguchi, D., Kitasaka, T., Iwano, S., Imaizumi, K., Hasegawa, Y., Suenaga, Y., Mori, K.: Automatic mediastinal lymph node detection in chest CT. In: SPIE, vol. 7260, p. 30 (2009)
Kiraly, A.P., Naidich, D.P., Guendel, L., Zhang, L., Novak, C.L.: Novel method and applications for labeling and identifying lymph nodes. In: SPIE (2007)
Kitasaka, T., Tsujimura, Y., Nakamura, Y., Mori, K., Suenaga, Y., Ito, M., Nawano, S.: Automated extraction of lymph nodes from 3-d abdominal ct images using 3-d minimum directional difference filter. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, p. 336. Springer, Heidelberg (2007)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV 77, 259–289 (2008)
Seifert, S., Barbu, A., Zhou, S.K., Liu, D., Feulner, J., Huber, M., Suehling, M., Cavallaro, A., Comaniciu, D.: Hierarchical parsing and semantic navigation of full body CT data. In: SPIE Medical Imaging (2009)
Tu, Z., Zhou, X.S., Barbu, A., Bogoni, L., Comaniciu, D.: Probabilistic 3D polyp detection in CT images: The role of sample alignment. In: CVPR (2006)
Yan, J., Zhuang, T., Zhao, B., Schwartz, L.H.: Lymph node segmentation from CT images using fast marching method. Computerized Medical Imaging and Graphics 28(1-2), 33–38 (2004)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE TMI 27(11) (2008)
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Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S.K., Comaniciu, D. (2010). Automatic Detection and Segmentation of Axillary Lymph Nodes. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_4
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DOI: https://doi.org/10.1007/978-3-642-15705-9_4
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