Abstract
In this paper, we demonstrate how a semi-automatic algorithm we proposed in previous work may be integrated into a protocol which becomes fully automatic for the detection of brain metastases. Such a protocol combines 11C-labeled Methionine PET acquisition with our previous segmentation approach. We show that our algorithm responds especially well to this modality thereby upgrading its status from semi-automatic to fully automatic for the presented application. In this approach, the active contour method is based on the minimization of an energy functional which integrates the information provided by a machine learning algorithm. The rationale behind such a coupling is to introduce in the segmentation the physician knowledge through a component capable of influencing the final outcome toward what would be the segmentation performed by a human operator. In particular, we compare the performance of three different classifiers: Naïve Bayes classification, K-Nearest Neighbor classification, and Discriminant Analysis. A database comprising seventeen patients with brain metastases is considered to assess the performance of the proposed method in the clinical environment.
Regardless of the classifier used, automatically delineated lesions show high agreement with the gold standard (R2 = 0.98). Experimental results show that the proposed protocol is accurate and meets the physician requirements for radiotherapy treatment purpose.
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References
Comelli, A., et al.: Automatic multi-seed detection for MR breast image segmentation. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 706–717. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68560-1_63
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
Grosu, A.L., Weber, W.A., Franz, M., et al.: Reirradiation of recurrent high-grade gliomas using amino acid PET (SPECT)/CT/MRI image fusion to determine gross tumor volume for stereotactic fractionated radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 63, 511–519 (2005). https://doi.org/10.1016/j.ijrobp.2005.01.056
Borasi, G., Russo, G., Alongi, F., et al.: High-intensity focused ultrasound plus concomitant radiotherapy: a new weapon in oncology? J. Ther. Ultrasound 1, 6 (2013). https://doi.org/10.1186/2050-5736-1-6
Banna, G.L., Anile, G., Russo, G., et al.: Predictive and prognostic value of early disease progression by PET evaluation in advanced non-small cell lung cancer. Oncol. (2017). https://doi.org/10.1159/000448005
Angulakshmi, M., Lakshmi Priya, G.G.: Automated brain tumour segmentation techniques—a review. Int. J. Imaging Syst. Technol. (2017). https://doi.org/10.1002/ima.22211
Zaidi, H., El Naqa, I.: PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur. J. Nucl. Med. Mol. Imaging 37, 2165–2187 (2010). https://doi.org/10.1007/s00259-010-1423-3
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
Khadidos, A., Sanchez, V., Li, C.T.: Weighted level set evolution based on local edge features for medical image segmentation. IEEE Trans. Image Process. (2017). https://doi.org/10.1109/TIP.2017.2666042
Göçeri, E.: Fully automated liver segmentation using Sobolev gradient-based level set evolution. Int. J. Numer. Method Biomed. Eng. (2016). https://doi.org/10.1002/cnm.2765
Min, H., Jia, W., Zhao, Y., et al.: LATE: a level-set method based on local approximation of taylor expansion for segmenting intensity inhomogeneous images. IEEE Trans. Image Process. (2018). https://doi.org/10.1109/TIP.2018.2848471
Goceri, E., Dura, E.: A level set method with Sobolev gradient and Haralick edge detection. In: 4th World Conference on Information Technology (WCIT 2013), vol. 5, pp. 131–140 (2013)
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
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
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., 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
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., 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
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
Comelli, A., Stefano, A., Benfante, V., Russo, G.: Normal and abnormal tissue classification in PET oncological studies. Pattern Recognit. Image Anal. 28, 121–128 (2018). https://doi.org/10.1134/S1054661818010054
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
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
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
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
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15, 29 (2015). https://doi.org/10.1186/s12880-015-0068-x
Hatt, M., Laurent, B., Ouahabi, A., et al.: The first MICCAI challenge on PET tumor segmentation. Med. Image Anal. 44, 177–195 (2018). https://doi.org/10.1016/j.media.2017.12.007
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
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., 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
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Comelli, A. et al. (2020). Tissue Classification to Support Local Active Delineation of Brain Tumors. 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_1
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