Abstract
One of the key challenges facing wider adoption of positron emission tomography (PET) as an imaging biomarker of disease is the development of reproducible quantitative image interpretation tools. Quantifying changes in tumor tissue, due to disease progression or treatment regimen, often requires accurate and reproducible delineation of lesions. Lesion segmentation is necessary for measuring tumor proliferation/shrinkage and radiotracer-uptake to quantify tumor metabolism. In this paper, we develop a fully automatic method for lesion delineation, which does not require user-initialization or parameter-tweaking, to segment novel PET images. To achieve this, we train a machine learning system on anatomically and physiologically meaningful imaging cues, to distinguish normal organ activity from tumorous lesion activity. The inferred lesion likelihoods are then used to guide a convex segmentation model, guaranteeing reproducible results. We evaluate our approach on datasets from The Cancer Imaging Archive trained on data from the Quantitative Imaging Network challenge that were delineated by multiple users. Our method not only produces more accurate segmentation than state-of-the art segmentation results, but does so without any user interaction.
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
Notes
- 1.
A method is described as non-reproducible when its results are dependent on image-specific parameter tuning/initialization or other user interaction.
References
Abdoli, M., et al.: Contourlet-based active contour model for PET image segmentation. Med. Phys. 40(8), 082507: 1–082507: 12 (2013)
Bagci, U., et al.: 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)
Bi, L., Kim, J., Feng, D., Fulham, M.: Multi-stage thresholded region classification for whole-body PET-CT lymphoma studies. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 569–576. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10404-1_71
Bresson, X., et al.: Fast global minimization of the active contour/snake model. J. Math. Imaging Vis. 28(2), 151–167 (2007)
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28(1), 45–62 (2002)
Cui, H., et al.: Primary lung tumor segmentation from PET-CT volumes with spatial-topological constraint. Int. J. Comput. Assist. Radiol. Surg. 11(1), 19–29 (2015)
Dewalle-Vignion, A., et al.: A new method for volume segmentation of PET images, based on possibility theory. IEEE Trans. Med. Imag. 30(2), 409–423 (2011)
Fedorov, A., et al.: DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4, e2057 (2016)
Foster, B., et al.: Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models. IEEE Trans. Biomed. Eng. 61(3), 711–724 (2014)
Foster, B., et al.: A review on segmentation of positron emission tomography images. Comput. Biol. Med. 50, 76–96 (2014)
Haralick, R.M., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Hatt, M., et al.: Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int. J. Radiat. Oncol. Biol. Phys. 77(1), 301–308 (2010)
Ju, W., et al.: Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Trans. Image Process. 24(12), 5854–5867 (2015)
Kumar, A., et al.: A graph-based approach for the retrieval of multi-modality medical images. Med. Image Anal. 18(2), 330–342 (2014)
Lapuyade-Lahorgue, J., et al.: Speqtacle: an automated generalized fuzzy c-means algorithm for tumor delineation in PET. Med. Phys. 42(10), 5720–5734 (2015)
Layer, T., et al.: PET image segmentation using a Gaussian mixture model and Markov random fields. EJNMMI Phys. 2(1), 1–15 (2015)
Lelandais, B., Gardin, I., Mouchard, L., Vera, P., Ruan, S.: Segmentation of biological target volumes on multi-tracer PET images based on information fusion for achieving dose painting in radiotherapy. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 545–552. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33415-3_67
Liaw, A., et al.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)
Nestle, U., et al.: 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)
Soh, L.K., et al.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780–795 (1999)
Song, Q., et al.: Optimal co-segmentation of tumor in PET-CT images with context information. IEEE Trans. Med. Imag. 32(9), 1685–1697 (2013)
Yu, H., et al.: Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. Int. J. Radiat. Oncol. Biol. Phys. 75(2), 618–625 (2009)
Zeng, Z., et al.: Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting. Comput. Biol. Med. 43(10), 1530–1544 (2013)
Acknowledgements
Funding provided by the Canadian Institutes of Health Research (OQI-137993).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Ahmadvand, P., Duggan, N., Bénard, F., Hamarneh, G. (2016). Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-47157-0_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47156-3
Online ISBN: 978-3-319-47157-0
eBook Packages: Computer ScienceComputer Science (R0)