Abstract
Positron Emission Tomography (PET) is widely used for lymphoma detection. It is often combined with the CT scan in order to provide anatomical information for helping lymphoma detection. Two common types of approaches can be distinguished for lymphoma detection and segmentation in PET. The first one is ROI dependent which needs a ROI defined by physicians who firstly detect where lymphomas are. The second one is based on machine learning methods which need a large learning database. However, such a large standard database is quite rare in medical field. Considering these problems, we propose a new approach which combines a multi-atlas segmentation of the CT with CRFs (Conditional Random Fields) segmentation method in PET. It consists of 3 steps. Firstly, an anatomical multi-atlas segmentation is applied on CT to locate and remove the organs having hyper metabolism in PET. Secondly, CRFs detect and segment the lymphoma regions in PET. The conditional probabilities used in CRFs are usually estimated by a learning step. In this work, we propose to estimate them in an unsupervised way. A list of the detected regions in 3D is visualized. The final step is to select real lymphomas by simply clicking on them. Our method is tested on ten patients. The rate of good detection is 100%. The average of Dice index over 10 patients for measuring the lymphoma is 80% compared to manual lymphoma segmentation. Comparing with other methods in terms of Dice index shows the best performance of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zaidi, H., El Naqa, I.: PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur. J. Nucl. Med. Mol. Imaging 37, 2165–2187 (2010)
Desbordes, P., Petitjean, C., Ruan, S.: 3D automated lymphoma segmentation in PET images based on cellular automata. IEEE, (2015). Electronic ISSN:2154-512X
Eloïse, G., Hugues, T., Nicolas, P., Michel, M., Laurent, N.: Automated 3D lymphoma lesion segmentation from PET/CT characteristics. In: Symposium on Biomedical Imaging: From Nano to Macro, pp. 174–178 (2017)
Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comput. Vis. 81, 2–23 (2007)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. Adv. Neural. Inf. Process. Syst. 24, 109–117 (2011)
Boykov, Y.Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. IEEE (2011). doi:10.1109/ICCV.2001.937505
Krähenbühl, P., Koltun, V.: Parameter learning and convergent inference for dense random fields. In: International Conference on Machine Learning (ICML) (2013)
Black, Q.C., Grills, I.S., Kestin, L.L., Wong, C.Y., Wong, J.W., Martinez, A.A., Yan, D.: Defining a radiotherapy target with positron emission tomography. Int. J. Radiat. Oncol. Biol. Phys. 60(4), 1272–1282 (2004)
Nestle, U., Kremp, S., Schaefer-Schuler, A., Sebastian-Welsch, C., Hellwig, D., Rübe, C., Kirsch, C.M.: Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J. Nucl. Med. 46(8), 1342–1348 (2005)
Vauclin, S., Doyeux, K., Hapdey, S., Edet-Sanson, A., Vera, P., Gardin, I.: Development of a generic thresholding algorithm for the delineation of 18FDG-PET-positive tissue: application to the comparison of three thresholding models. Phys. Med. Biol. 54(22), 6901–6916 (2009)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut -interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (SIGGRAPH) (2004)
Yan, T., Liu, Q., Wei, Q., Chen, F., Deng, T.: Classification of lymphoma cell image based on improved SVM. In: Zhang, T.-C., Nakajima, M. (eds.) Advances in Applied Biotechnology. LNEE, vol. 332, pp. 199–208. Springer, Heidelberg (2015). doi:10.1007/978-3-662-45657-6_21
Sharif, M.S., Amira, A., Zaidi, H.: 3D oncological PET volume analysis using CNN and LVQNN. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems (ISCAS 2010), pp. 1783–1786, June 2010
Zhoubing, X.U., Ryan, R.P., Lee, C.P., Baucom, R.B., Poulose, B.K., Abramson, R.G., Landman, B.A.: Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning. Med. Image Anal. 24(1), 18–27 (2015)
Tylski, P., Stute, S., Grotus, N., Doyeux, K., Hepdey, S., Gardin, I., Vanderlinden, B., Buvat, I.: Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18) F-FDG PET. J. Nucl. Med. 51, 268–276 (2010)
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.A.: comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 1068–1080 (2008)
Weiler-Sagie, M., Bushelev, O., Epelbaum, R., Dann, E.J., Haim, N., Avivi, I., Ben-Barak, A., Ben-Arie, Y., Bar-Shalom, R., Israel, O.: (18) F-FDG avidity in lymphoma readdressed: a study of 766 patients. J. Nucl. Med. 51(1), 25–30 (2009)
Cottereau, A.-S., Lanic, H., Mareschal, S., Meignan, M., Vera, P., Tilly, H., Jardin, F., Becker, S.: Molecular profile and FDG-PET/CT total metabolic tumor volume improve risk classification at diagnosis for patients with diffuse large b-cell lymphoma. Clin. Cancer Res. 22(15), 3801–3809 (2016)
Meignan, M., Sasanelli, M., Casasnovas, R.O., Luminari, S., Fioroni, F., Coriani, C., Masset, H., Itti, E., Gobbi, P.G., Merli, F., Versari, A.: Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients. Eur. J. Nucl. Med. Mol. Imaging 41(6), 1113–1122 (2014)
Meignan, M., Gallamini, A., Meignan, M., Gallamini, A., Haioun, C.: Report on the first international workshop on interim-PET scan in lymphoma. Leuk. Lymphoma 50(8), 1257–1260 (2009)
Barrington, S.F., Kluge, R.: FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas. Eur. J. Nucl. Med. Mol. Imaging (2017). doi:10.1007/s00259-017-3690-8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yu, Y., Decazes, P., Gardin, I., Vera, P., Ruan, S. (2017). 3D Lymphoma Segmentation in PET/CT Images Based on Fully Connected CRFs. In: Cardoso, M., et al. Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. RAMBO CMMI SWITCH 2017 2017 2017. Lecture Notes in Computer Science(), vol 10555. Springer, Cham. https://doi.org/10.1007/978-3-319-67564-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-67564-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67563-3
Online ISBN: 978-3-319-67564-0
eBook Packages: Computer ScienceComputer Science (R0)