Abstract
Positron emission tomography – computed tomography (PET-CT) has been widely used in modern cancer imaging. Accurate tumor delineation from PET and CT plays an important role in radiation therapy. The PET-CT co-segmentation technique, which makes use of advantages of both modalities, has achieved impressive performance for tumor delineation. In this work, we propose a novel 3D image matting based semi-automated co-segmentation method for tumor delineation on dual PET-CT scans. The “matte” values generated by 3D image matting are employed to compute the region costs for the graph based co-segmentation. Compared to previous PET-CT co-segmentation methods, our method is completely data-driven in the design of cost functions, thus using much less hyper-parameters in our segmentation model. Comparative experiments on 54 PET-CT scans of lung cancer patients demonstrated the effectiveness of our method.
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Hatt, M., Tixier, F., Pierce, L., Kinahan, P., Le Rest, C., Visvikis, D.: Characterization of PET/CT images using texture analysis: the past, the present... any future? Eur. J. Nucl. Med. Mol. Imaging 44(1), 151–166 (2017)
Bagci, U., Udupa, J.K., Mendhiratta, N., Foster, B., Xu, Z., Yao, J., Chen, X., Mollura, D.J.: Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images. Med. Image Anal. 17(8), 929–945 (2013)
Foster, B., Bagci, U., Mansoor, A., Xu, Z., Mollura, D.J.: A review on segmentation of positron emission tomography images. Comput. Biol. Med. 50, 76–96 (2014)
Steenbakkers, R.J.H.M., Duppen, J.C., Fitton, I., Deurloo, K.E.I., Zijp, L.J., Comans, E.F.I., Uitterhoeve, A.L.J., Rodrigus, P.T.R., Kramer, G.W.P., Bussink, J., De Jaeger, K., Belderbos, J.S.A., Nowak, P.J.C.M., van Herk, M., Rasch, C.R.N.: Reduction of observer variation using matched CT-PET for lung cancer delineation: a theree-dimensional analysis. Int. J. Radiat. Oncol. Biol. Phys. 64, 435–448 (2006)
Fiorino, C., Reni, M., Bolognesi, A., Cattaneo, G., Calandrino, R.: Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. Int. J. Radiat. Oncol. Biol. Phys. 4, 285–292 (1998)
Chang, J., Joon, D.L., Lee, S., Gong, S., Anderson, N., Scott, A., Davis, I., Clouston, D., Bolton, D., Hamilton, C., Khoo, V.: Intensity modulated radiation therapy dose painting for localized prostate cancer using \(^{11}\)C-choline positron emission tomography scans. Int. J. Radiat. Oncol. Biol. Phys. 83(5), 691–696 (2012)
Breen, S.L., Publicover, J., De Silva, S., Pond, G., Brock, K., OSullivan, B., Cummings, B., Dawson, L., Keller, A., Kim, J., Ringash, J., Yu, E., Hendler, A., Waldron, J.: Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers. Int. J. Radiat. Oncol. Biol. Phys. 68, 763–770 (2007)
Hong, R., Halama, J., Bova, D., Sethi, A., Emami, B.: Correlation of PET standard uptake value and CT window-level thresholds for target delineation in CT-based radiation treatment planning. Int. J. Radiat. Oncol. Biol. Phys. 67, 720–726 (2007)
Erdi, Y.E., Mawlawi, O., Larson, S.M., Imbriaco, M., Yeung, H., Finn, R., Humm, J.L.: Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer 80, 2505–2509 (1997)
Miller, T.R., Grigsby, P.W.: Measurement of tumor volume by PET to evaluate prognosis in patients with advanced cervical cancer treated by radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 53, 353–359 (2002)
Nehmeh, S.A., El-Zeftawy, H., Greco, C., Schwartz, J., Erdi, Y.E., Kirov, A., Schmidtlein, C.R., Gyau, A.B., Larson, S.M., Humm, J.L.: An iterative technique to segment PET lesions using a Monte Carlo based mathematical model. Med. Phys. 36, 4803–4809 (2009)
Drever, L., Roa, W., McEwan, A., Robinson, D.: Comparison of three image segmentation techniques for target volume delineation in positron emission tomography. J. Appl. Clin. Med. Phys. 8, 93–109 (2007)
Geets, X., Lee, J., Bol, A., Lonneux, M., Gregoire, V.: A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur. J. Nucl. Med. Mol. Imaging 34, 1427–1438 (2007)
Hsu, C.Y., Liu, C.Y., Chen, C.M.: Automatic segmentation of liver PET images. Med. Phys. 32, 601–610 (2008)
Li, H., Thorstad, W.L., Biehl, K.J., Laforest, R., Su, Y., Shoghi, K.I., Donnelly, E.D., Low, D.A., Lu, W.: A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours. Med. Phys. 35, 3711–3721 (2008)
El Naqa, I., Yang, D., Apte, A., Khullar, D., Mutic, S., Zheng, J., Bradley, J.D., Grigsby, P., Deasy, J.O.: Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. Med. Phys. 34(12), 4738–4749 (2007)
Grubben, H., Miller, P., Hanna, G., Carson, K., Hounsell, A.: MAP-MRF segmentation of lung tumours in PET-CT image. In: Proceedings of International Symposium on Biomedical Imaging, pp. 290–293 (2009)
Han, D., Bayouth, J., Song, Q., Taurani, A., Sonka, M., Buatti, J., Wu, X.: Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 245–256. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22092-0_21
Aristophanous, M., Penney, B., Martel, M., Pelizzari, C.: A gaussian mixture model for definition of lung tumor volumes in positron emission tomography. Med. Phys. 34, 4223–4235 (2007)
Bagci, U., Udupa, J.K., Yao, J., Mollura, D.J.: Co-segmentation of functional and anatomical images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 459–467. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33454-2_57
Foster, B., Bagci, U., Luna, B., Dey, B., Bishai, W., Jain, S., Xu, Z., Mollura, D.: Robust segmentation and accurate target definition for positron emission tomography images using affinity propagation. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 1461–1464, April 2013
Hatt, M., le Rest, C.C., Turzo, A., Roux, C., Visvikis, D.: A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans. Med. Imaging 28, 881–893 (2009)
Song, Q., Bai, J., Han, D., Bhatia, S., Sun, W., Rockey, W., Bayouth, J.E., Buatti, J.M., Wu, X.: Optimal co-segmentation of tumor in PET-CT images with context information. IEEE Trans. Med. Imaging 32(9), 1685–1697 (2013)
Li, H., Bai, J., Hejle, T.A., Wu, X., Bhatia, S., Kim, Y.: Automated cosegmentation of tumor volume and metabolic activity using PET-CT in non-small cell lung cancer (NSCLC). Int. J. Radiat. Oncol. Biol. Phys. 87(2), S528 (2013)
Lartizien, C., Rogez, M., Niaf, E., Ricard, F.: Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information. IEEE J. Biomed. Health Inf. 18(3), 946–955 (2014)
Ju, W., Xiang, D., Zhang, B., Wang, L., Kopriva, I., Chen, X.: Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Trans. Image Process. 24(12), 5854–5867 (2015)
Shao, H.C., Cheng, W.Y., Chen, Y.C., Hwang, W.L.: Colored multi-neuron image processing for segmenting and tracing neural circuits. In: 2012 19th IEEE International Conference on Image Processing, pp. 2025–2028, September 2012
Zeng, Z., Zwiggelaar, R.: Segmentation for multiple sclerosis lesions based on 3D volume enhancement and 3D alpha matting. In: Kamel, M., Campilho, A. (eds.) ICIAR 2013. LNCS, vol. 7950, pp. 573–580. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39094-4_65
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)
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This research was supported in part by the NIH Grant R21CA209874.
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Zhong, Z., Kim, Y., Buatti, J., Wu, X. (2017). 3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images. 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_4
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