Abstract
Lymphoma is a cancer of the lymphatic system, and it can affect many organs throughout the body. Positron emission tomography (PET)/computed tomography (CT) are primary imaging methods to assess lymphoma types and monitor their treatment, where PET is sensitive to identify lymphoma regions while CT preserves anatomical structures. Combining PET and CT is thus useful for lymphoma segmentation because it helps to identify lymphoma types and evaluate treatment effects. However, lymphoma segmentation suffers many challenges, including substantial lymphoma size and shape variance, numerous types, limited PET/CT data for lymphoma, and similar PET signals with adjacent organs. To address these challenges, we integrate label guidance, patch sampling, and negative data augmentation to achieve multi-modal lymphoma segmentation. The training data consist of positive and negative patch samples. These samples are purposely extracted from the original scans with the guidance of lymphoma labels. Negative samples are further supplemented from the PET/CT scans of non-lymphoma patients to better discriminate lymphoma from adjacent organs. The proposed method was validated on the PET/CT scans from 28 patients. Experimental results revealed that the Dice coefficient was improved from 0.11 to 0.43 in comparison with a baseline method the 3D-residual U-Net method. Patch-based strategy is also computational undemanding. These results suggest that the proposed method could be an efficient means to segment lymphoma and possibly used for identifying lymphoma types and assessing their treatment.
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References
A predictive model for aggressive Non-Hodgkin’s lymphoma. N. Engl. J. Med. 329(14), 987–994 (1993). https://doi.org/10.1056/NEJM199309303291402
Czernin, J., Allen-Auerbach, M., Nathanson, D., Herrmann, K.: PET/CT in oncology: current status and perspectives. Curr. Radiol. Rep. 1(3), 177–190 (2013)
Huang, L., Ruan, S., Decazes, P., Denœux, T.: Evidential segmentation of 3D PET/CT images. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds.) BELIEF 2021. LNCS (LNAI), vol. 12915, pp. 159–167. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88601-1_16
Juweid, M.E., Cheson, B.D.: Positron-emission tomography and assessment of cancer therapy. N. Engl. J. Med. 354(5), 496–507 (2006)
Kim, C.K., Gupta, N.C., Chandramouli, B., Alavi, A.: Standardized uptake values of FDG: body surface area correction is preferable to body weight correction. J. Nucl. Med. 35(1), 164–167 (1994)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6980
Li, H., et al.: DenseX-Net: an end-to-end model for lymphoma segmentation in whole-body PET/CT images. IEEE Access 8, 8004–8018 (2019)
Li, J., Xiao, Y.: Application of FDG-PET/CT in radiation oncology. Front. Oncol. 3, 80 (2013)
Liu, L., Nie, F., Wiliem, A., Li, Z., Zhang, T., Lovell, B.C.: Multi-modal joint clustering with application for unsupervised attribute discovery. IEEE Trans. Image Process. 27(9), 4345–4356 (2018)
Noy, A., et al.: The majority of transformed lymphomas have high standardized uptake values (SUVs) on positron emission tomography (PET) scanning similar to diffuse large b-cell lymphoma (DLBCL). Ann. Oncol. 20(3), 508–512 (2009)
Weisman, A.J., et al.: Automated quantification of baseline imaging pet metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients. EJNMMI Phys. 7(1), 1–12 (2020)
Xu, P., Zhu, X., Clifton, D.A.: Multimodal learning with transformers: a survey. arXiv preprint arXiv:2206.06488 (2022)
Acknowledgements
This research was supported by the National Institutes of Health, Clinical Center and by a Cooperative Research and Development Agreement with Ping An.
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Liu, L. et al. (2022). Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans. In: Li, X., Lv, J., Huo, Y., Dong, B., Leahy, R.M., Li, Q. (eds) Multiscale Multimodal Medical Imaging. MMMI 2022. Lecture Notes in Computer Science, vol 13594. Springer, Cham. https://doi.org/10.1007/978-3-031-18814-5_12
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DOI: https://doi.org/10.1007/978-3-031-18814-5_12
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