Abstract
Bone tumor segmentation on bone scans has recently been adopted as a basis for objective tumor assessment in several phase II and III clinical drug trials. Interpretation can be difficult due to the highly sensitive but non-specific nature of bone tumor appearance on bone scans. In this paper we present a machine learning approach to segmenting tumors on bone scans, using intensity and context features aimed at addressing areas prone to false positives. We computed the context features using landmark points, identified by a modified active shape model. We trained a random forest classifier on 100 and evaluated on 73 prostate cancer subjects from a multi-center clinical trial. A reference segmentation was provided by a board certified radiologist. We evaluated our learning based method using the Jaccard index and compared against the state of the art, rule based method. Results showed an improvement from 0.50 ±0.31 to 0.57 ±0.27. We found that the context features played a significant role in the random forest classifier, helping to correctly classify regions prone to false positives.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Mundy, G.: Metastasis to Bone: Causes, Consequences and Therapeutic Opportunities. Nat. Rev. Cancer. 2, 584–593 (2002)
Coleman, R.: Clinical Features of Metastatic Bone Disease and Risk of Skeletal Morbidity. Clin. Cancer Res. 12, 6243s (2006)
Sonpavde, G., Pond, G., Berry, W., Wit, R., Eisenberger, M., Tannock, I., Armstrong, A.: The Association Between Radiographic Response and Overall Survival in Men with Metastatic Castration-Resistant Prostate Cancer Receiving Chemotherapy. Cancer 117, 3963–3971 (2011)
Sadik, M., Suurkula, M., Hoglund, P., Jarund, A., Edenbrandt, L.: Quality of Planar Whole-body Bone Scan Interpretations – A Nationwide Survey. Eur. J. Nucl. Med. Mol. Im. 35(8), 1464–1472 (2008)
Bombardieri, E., Aktolun, C., Baum, R., Maffioli, L., Moncayo, R., Mortelmans, L., Reske, S.: Bone Scintigraphy: Procedure Guidelines for Tumour Imaging. Eur. J. Nucl. Med. Mol. Im. 30, 99–106 (2003)
Larson, S., Nelp, W.: The Radiocolloid Bone Marrow Scan in Malignant Disease. J. Surgical Onc. 3(6), 685–697 (1971)
Holder, L., Collier, D., Fogelman, I.: An Atlas of Planar and SPECT Bone Scans. CRC Press (2000)
Brown, M., Chu, G., Kim, H., Allen-Auerbach, M., Poon, C., Bridges, J., Vidovic, A., Ramakrishna, B., Ho, J., Morris, M., Larson, S., Scher, H., Goldin, J.: Computer-Aided Quantitative Bone Scan Assessment of Prostate Cancer Treatment Response. Nucl. Med. Commun. 33(4), 384–394 (2012)
Scher, H., Smith, M., Sweeney, C., Corn, P., Logothetis, C., Vogelzang, N., Smith, D., Hussain, M., George, D., Bono, J., Higano, C., Small, E., Goldin, J., Brown, M., Aftab, D., Noursalehi, M., Weitzman, A., Basch, E.: An Exploratory Analysis of Bone Scan Lesion Area, Circulating Tumor Cell change, Pain Reduction, and Overall Survival in Patients with Castration-Resistant Prostate Cancer Treated with Cabozantinib. J. Clin. Onc. 31(15), 5026 (2013)
Chu, G., Lo, P., Kim, H., Auerbach, M., Goldin, J., Henkel, K., Banola, A., Morris, D., Coy, H., Brown, M.: Preliminary Results of Automated Removal of Degenerative Joint Disease in Bone Scan Lesion Segmentation. In: Proc. SPIE 8670 Medical Imaging, 867007 (2013)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active Shape Models - Their Training and Application. Comp. Vis. and Im. Und. 61(1), 38–59 (1995)
Dalal, N., Triggs, B.: Histogram of Oriented Gradients for Human Detection. In: CVPR, pp. 886–893 (2005)
Vedaldi, A., Fulkerson, B.: VLFeat: An Open and Portable Library of Computer Vision Algorithms, http://www.vlfeat.org/
Haralick, R., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Sys. Man and Cyb. 6, 610–621 (1973)
Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer Academic Publishers (1994)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Bruncher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. J. of Mach. Learn. Res. 12, 2825–2830 (2011)
Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and Regression Trees. Chapman and Hall/CRC (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chu, G. et al. (2014). Bone Tumor Segmentation on Bone Scans Using Context Information and Random Forests. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_75
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_75
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)