Abstract
In this article a new methodology is proposed to tackle the problem of automatic segmentation of low-grade gliomas. The possibility of knowing the limits of this type of tumor is crucial for effectively characterizing the neoplasm, enabling, in certain cases, to obtain useful information about how to treat the patient in a more effective way. Using a database of magnetic resonance images, containing several occurrences of this type of tumors, and through a carefully designed image processing pipeline, the purpose of this work is to accurately locate, isolate and thus facilitate the classification of the pathology. The proposed methodology, described in detail, was able to achieve an accuracy of 87.5% for a binary classification task. The quality of the identified regions had an accuracy of 81.6%. These are promising results that may point the effectiveness of the approach. The low contrast of the images, as a result of the acquisition process, and the detection of very small tumors are still challenges that bring motivation to further pursue additional results.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gaspar, B.M.: Biomarcadores em gliomas: conhecimento atual e perspetivas futuras (2016). http://hdl.handle.net/10316/46899
Louis, D.N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W.K., Ohgaki, H., Wiestler, O.D., Kleihues, P., Ellison, D.W.: The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. (Berl.) 131, 803–820 (2016)
Forst, D.A., Nahed, B.V., Loeffler, J.S., Batchelor, T.T.: Low-grade gliomas. Oncologist. 19, 403–413 (2014)
de Goulart, B.N.G., Chiari, B.M.: Testes de rastreamento x testes de diagnóstico: atualidades no contexto da atuação fonoaudiológica. Pró-Fono Rev. Atualização Científica 19, 223–232 (2007)
Bø, H.K., Solheim, O., Jakola, A.S., Kvistad, K.-A., Reinertsen, I., Berntsen, E.M.: Intra-rater variability in low-grade glioma segmentation. J. Neurooncol. 131, 393–402 (2017)
Guillevin, R., Herpe, G., Verdier, M., Guillevin, C.: Low-grade gliomas: the challenges of imaging. Diagn. Interv. Imaging 95, 957–963 (2014)
Ostrom, Q.T., Gittleman, H., Xu, J., Kromer, C., Wolinsky, Y., Kruchko, C., Barnholtz-Sloan, J.S.: CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009-2013. Neuro-Oncology 18, v1–v75 (2016)
Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.: GLISTR: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31, 1941–1954 (2012)
Rathore, S., Bakas, S., Pati, S., Akbari, H., Kalarot, R., Sridharan, P., Rozycki, M., Bergman, M., Tunc, B., Verma, R., Bilello, M., Davatzikos, C.: Brain cancer imaging phenomics toolkit (brain-CaPTk): an interactive platform for quantitative analysis of glioblastoma. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Third International Workshop, BrainLes 2017, Held in Conjunction MICCAI 2017, Quebec City, QC, Canada, 14 September 2017, Revised Selected Papers, vol. 10670, pp. 133–145 (2018)
Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., Davatzikos, C.: GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, First International Workshop, Brainles 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, 5 October 2015, Revised Selected Papers, vol. 9556, pp. 144–155 (2016)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Akkus, Z., Ali, I., Sedlář, J., Agrawal, J.P., Parney, I.F., Giannini, C., Erickson, B.J.: Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J. Digit. Imaging 30, 469–476 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Barbosa, M., Moreira, P., Ribeiro, R., Coelho, L. (2020). Automatic Classification and Segmentation of Low-Grade Gliomas in Magnetic Resonance Imaging. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-17065-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17064-6
Online ISBN: 978-3-030-17065-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)