Abstract
In this paper, we present an automatic system for the brain metastasis delineation in Positron Emission Tomography images. The segmentation process is fully automatic, so that intervention from the user is never required making the entire process completely repeatable. Contouring is performed using an enhanced local active segmentation.
The proposed system is, at first instance, evaluated on four datasets of phantom experiments to assess the performance under different contrast ratio scenarios, and, successively, on ten clinical cases in radiotherapy environment.
Phantom studies show an excellent performance with a dice similarity coefficient rate greater than 92% for larger spheres. In clinical cases, automatically delineated tumors show high agreement with the gold standard with a dice similarity coefficient of 88.35 ± 2.60%.
These results show that the proposed system can be successfully employed in Positron Emission Tomography images, and especially in radiotherapy treatment planning, to produce fully automatic segmentations of brain cancers.
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References
Comelli, A., Bruno, A., Di Vittorio, M.L., et al.: Automatic Multi-seed Detection for MR Breast Image Segmentation, pp. 706–717. Springer, Cham (2017)
Chandarana, H., Wang, H., Tijssen, R.H.N., Das, I.J.: Emerging role of MRI in radiation therapy. J. Magn. Reson. Imaging 48, 1468–1478 (2018). https://doi.org/10.1002/jmri.26271
Agnello, L., Comelli, A., Ardizzone, E., Vitabile, S.: Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis. Int. J. Imaging Syst. Technol. (2016). https://doi.org/10.1002/ima.22168
Astner, S.T., Dobrei-Ciuchendea, M., Essler, M., et al.: Effect of 11C-methionine-positron emission tomography on gross tumor volume delineation in stereotactic radiotherapy of skull base meningiomas. Int. J. Radiat. Oncol. Biol. Phys. 72, 1161–1167 (2008). https://doi.org/10.1016/j.ijrobp.2008.02.058
Comelli, A., Stefano, A., Bignardi, S., et al.: Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography. Artif. Intell. Med. 94, 67–78 (2019). https://doi.org/10.1016/J.ARTMED.2019.01.002
Comelli, A., Stefano, A., Russo, G., et al.: K-nearest neighbor driving active contours to delineate biological tumor volumes. Eng. Appl. Artif. Intell. 81, 133–144 (2019). https://doi.org/10.1016/j.engappai.2019.02.005
Hatt, M., Cheze Le Rest, C., Albarghach, N., et al.: PET functional volume delineation: a robustness and repeatability study. Eur. J. Nucl. Med. Mol. Imaging 38, 663–672 (2011). https://doi.org/10.1007/s00259-010-1688-6
Berthon, B., Spezi, E., Galavis, P., et al.: Toward a standard for the evaluation of PET-Auto-Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation. Med Phys. (2017). https://doi.org/10.1002/mp.12312
Foster, B., Bagci, U., Mansoor, A., et al.: A review on segmentation of positron emission tomography images. Comput. Biol. Med. 50, 76–96 (2014). https://doi.org/10.1016/j.compbiomed.2014.04.014
Comelli, A., Stefano, A., Russo, G., et al.: A smart and operator independent system to delineate tumours in Positron Emission Tomography scans. Comput. Biol. Med. (2018). https://doi.org/10.1016/J.COMPBIOMED.2018.09.002
Soret, M., Bacharach, S.L., Buvat, I.I.: Partial-volume effect in PET tumor imaging. J. Nucl. Med. 48, 932–945 (2007). https://doi.org/10.2967/jnumed.106.035774
Stefano, A., Vitabile, S., Russo, G., et al.: A fully automatic method for biological target volume segmentation of brain metastases. Int. J. Imaging Syst. Technol. 26, 29–37 (2016). https://doi.org/10.1002/ima.22154
Stefano, A., et al.: An automatic method for metabolic evaluation of gamma knife treatments. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9279, pp. 579–589. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23231-7_52
Lankton, S., Nain, D., Yezzi, A., Tannenbaum, A.: Hybrid geodesic region-based curve evolutions for image segmentation. In: Proceedings of the SPIE 6510, Medical Imaging 2007: Physics of Medical Imaging, 16 March 2007, p. 65104U (2007). https://doi.org/10.1117/12.709700
Udupa, J.K., Leblanc, V.R., Zhuge, Y., et al.: A framework for evaluating image segmentation algorithms. Comput. Med. Imaging Graph. 30, 75–87 (2006). https://doi.org/10.1016/j.compmedimag.2005.12.001
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23, 903–921 (2004). https://doi.org/10.1109/TMI.2004.828354
Lankton, S., Nain, D., Yezzi, A., Tannenbaum, A.: Hybrid geodesic region-based curve evolutions for image segmentation. 65104U (2007). https://doi.org/10.1117/12.709700
Day, E., Betler, J., Parda, D., et al.: A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med. Phys. 36, 4349–4358 (2009). https://doi.org/10.1118/1.3213099
Stefano, A., Vitabile, S., Russo, G., et al.: An enhanced random walk algorithm for delineation of head and neck cancers in PET studies. Med. Biol. Eng. Comput. 55, 897–908 (2017). https://doi.org/10.1007/s11517-016-1571-0
Belhassen, S., Zaidi, H.: A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med. Phys. 37, 1309–1324 (2010). https://doi.org/10.1118/1.3301610
Licari, L., et al.: Use of the KSVM-based system for the definition, validation and identification of the incisional hernia recurrence risk factors. Il Giornale di chirurgia 40(1), 32–38 (2019)
Agnello, L., Comelli, A., Vitabile, S.: Feature dimensionality reduction for mammographic report classification. In: Pop, F., Kołodziej, J., Di Martino, B. (eds.) Resource Management for Big Data Platforms. CCN, pp. 311–337. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44881-7_15
Comelli, A., et al.: A kernel support vector machine based technique for Crohn’s disease classification in human patients. In: Barolli, L., Terzo, O. (eds.) CISIS 2017. AISC, vol. 611, pp. 262–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61566-0_25
Comelli, A., Stefano, A., Benfante, V., Russo, G.: Normal and abnormal tissue classification in pet oncological studies. Pattern Recogn. Image Anal. 28, 121–128 (2018). https://doi.org/10.1134/S1054661818010054
Comelli, A., Agnello, L., Vitabile, S.: An ontology-based retrieval system for mammographic reports. In: Proceedings of IEEE Symposium Computers and Communication (2016). https://doi.org/10.1109/ISCC.2015.7405644
Acknowledgments
Authors would like to thank Prof. Anthony Yezzi, Dr. Samuel Bignardi, Dr. Giorgio Russo, MD. Maria Gabriella Sabini, and MD. Massimo Ippolito for their crucial support in the management of the proposed study.
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Comelli, A., Stefano, A. (2020). A Fully Automated Segmentation System of Positron Emission Tomography Studies. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_30
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