Abstract
Automatic detection of lung tumors and abnormal lymph nodes are useful in assisting lung cancer staging. This paper presents a novel detection method, by first identifying all abnormalities, then differentiating between lung tumors and abnormal lymph nodes based on their degree of overlap with the lung field and mediastinum. Regression-based appearance model and graph-based structure labeling are designed to estimate the actual lung field and mediastinum from the pathology-affected thoracic images adaptively. The proposed method is simple, effective and generalizable, and can be potentially applicable to other medical imaging domains as well. Promising results are demonstrated based on our evaluations on clinical PET-CT data sets from lung cancer patients.
Keywords
- Lung Tumor
- Reference Image
- Appearance Model
- Statistical Shape Model
- Abnormality Detection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download conference paper PDF
References
Saradhi, G., Gopalakrishnan, G., Roy, A., Mullick, R., Manjeshwar, R., Thielemans, K., Patil, U.: A Framework for Automated Tumor Detection in Thoracic FDG PET Images Using Texture-based Features. In: ISBI, pp. 97–100 (2009)
Gubbi, J., Kanakatte, A., Kron, T., Binns, D., Srinivasan, B., Mani, N., Palaniswami, M.: Automatic tumour volume delineation in respiratory-gated PET images. J. Med. Imag. Radia. Oncol. 55, 65–76 (2011)
Feulner, J., Zhou, S.K., Huber, M., Hornegger, J., Comaniciu, D.: Lymph Nodes Detection in 3-D Chest CT Using a Spatial Prior Probability. In: CVPR, pp. 2926–2932 (2010)
Feuerstein, M., Glocker, B., Kitasaka, T., Nakamura, Y., Iwano, S., Mori, K.: Mediastinal Atlas Creation from 3-D Chest Computed Tomography Images: Application to Automated Detection and Station Mapping of Lymph Nodes. Med. Image Anal. 16(1), 63–74 (2011)
Song, Y., Cai, W., Eberl, S., Fulham, M.J., Feng, D.: Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 191–198. Springer, Heidelberg (2011)
Sluimer, I., Prokop, M., van Ginneken, B.: Toward Automated Segmentation of the Pathological Lung in CT. IEEE Trans. Med. Imag. 24(8), 1025–1038 (2005)
Sofka, M., Wetzl, J., Birkbeck, N., Zhang, J., Kohlberger, T., Kaftan, J., Declerck, J., Zhou, S.K.: Multi-stage Learning for Robust Lung Segmentation in Challenging CT Volumes. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 667–674. Springer, Heidelberg (2011)
Chen, T., Vemuri, B.C., Rangarajan, A., Eisenschenk, S.J.: Mixture of Segmenters with Discriminative Spatial Regularization and Sparse Weight Selection. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 595–602. Springer, Heidelberg (2011)
Rousseau, F., Habas, P.A., Studholme, C.: Human Brain Labeling Using Image Similarities. In: CVPR, pp. 1081–1088 (2011)
Wu, D., Lu, L., Bi, J., Shinagawa, Y., Boyer, K., Krishnan, A., Salganicoff, M.: Stratified Learning of Local Anatomical Context for Lung Nodules in CT Images. In: CVPR, pp. 2791–2798 (2010)
Tropp, J.: Greed Is Good: Algorithmic Results for Sparse Approximation. IEEE Trans. Inform. Theory 50, 2231–2242 (2004)
Kolmogorov, V., Zabih, R.: What Energy Functions Can Be Minimized via Graph Cuts? IEEE Trans. Pattern Anal. Machine Intell. 26(2), 147–159 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, Y., Cai, W., Zhou, Y., Feng, D. (2012). Thoracic Abnormality Detection with Data Adaptive Structure Estimation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-33415-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33414-6
Online ISBN: 978-3-642-33415-3
eBook Packages: Computer ScienceComputer Science (R0)
