Automated 2D Segmentation of Prostate in T2-weighted MRI Scans

Authors

  • Justinas Jucevicius Vilnius University, Institute of Mathematics and Informatics Lithuania, 08663 Vilnius, Akademijos str. 4
  • Povilas Treigys Vilnius University, Institute of Mathematics and Informatics Lithuania, 08663 Vilnius, Akademijos str. 4
  • Jolita Bernataviciene Vilnius University, Institute of Mathematics and Informatics Lithuania, 08663 Vilnius, Akademijos str. 4
  • Ruta Briediene Vilnius University, National Cancer Institute Lithuania, 08660 Vilnius, Santariški¸u str. 1
  • Ieva Naruševiciute Vilnius University, National Cancer Institute Lithuania, 08660 Vilnius, Santariški¸u str. 1
  • Gintautas Dzemyda Vilnius University, Institute of Mathematics and Informatics Lithuania, 08663 Vilnius, Akademijos str. 4
  • Viktor Medvedev Vilnius University, Institute of Mathematics and Informatics Lithuania, 08663 Vilnius, Akademijos str. 4

Keywords:

computer image processing, 2D prostate segmentation, magnetic resonance imaging (MRI), T2-weighted scan

Abstract

The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. Computer image analysis methods play the key role here. The problem of automated prostate magnetic resonance (MR) image segmentation is burdened by the fact that MRI signal intensity is not standardized: field of view and image appearance is for a large part determined by acquisition protocol, field strength, coil profile and scanner type. Authors overview the most recent Prostate MR image segmentation challenge results and provide insights on T2-weighted MRI scan images automated prostate segmentation problem by comparing the best obtained automatic segmentation algorithms and applying them to 2D prostate segmentation case. The most important benefit of this research will have medical doctors involved in the management of the cancer.

References

Archip, N., Clatz, O., Whalen, S., Kacher, D., Fedorov, A., et al. (2007), Non-rigid alignment of preoperative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery, NeuroImage, DOI: 10.1016/j.neuroimage.2006.11.060 https://doi.org/10.1016/j.neuroimage.2006.11.060

Birkbeck, N., Zhang, J., Requardt, M., Kiefer, B., Gall, P., Kevin Zhou, S., (2012), Regionspecific hierarchical segmentation of MR prostate using discriminative learning, Proc. Med. Image Comput. Comput.-Assisted Intervention Conf. Prostate Segment. Challenge, 4-11.

Borenstein, E., Malik, J. (2006, June). Shape guided object segmentation. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 1: 969-976. https://doi.org/10.1109/cvpr.2006.276

Buteikien˙e, D., Paunksnis, A., Barzdžiukas, V., Bernatavicien˙e, J., Marcinkevicius, V., Treigys, P. (2012); Assessment of the optic nerve disc and excavation parameters of interactive and automated parameterization methods. Informatica, 23(3): 335-355.

Chandra, S., Dowling, J., Shen, K., Raniga, P., Pluim, J., Greer, P., Salvado, O., Fripp, J. (2012); Patient specific prostate segmentation in 3-D magnetic resonance images. IEEE Trans. Med. Imaging, 31: 1955-1964. https://doi.org/10.1109/TMI.2012.2211377

Cootes, T., Petrovi, C., Schestowitz, R., Taylor, C. (2005); Groupwise construction of appearance models using piece-wise affine deformations. 16th British Machine Vision Conference, 2:879-888. https://doi.org/10.5244/c.19.88

Costin, H., Bejinariu, S., & Costin, D. (2016); Biomedical Image Registration by means of Bacterial Foraging Paradigm. International Journal of Computers Communications & Control, 11(3): 331-347. https://doi.org/10.15837/ijccc.2016.3.1860

Dua, S., & Acharya, R. (Eds.). (2016); Data Mining in Biomedical Imaging, Signaling, and Systems, CRC Press, 2016.

Ghavami, P.K. (2014). Clinical Intelligence the Big Data Analytics Revolution in Healthcare: A Framework for Clinical and Business Intelligence Createspace Independent Pub., 2014.

Ghose, S., Oliver, A., Marti, R., Llado, X., Vilanova, J., et al. (2012); A Survey of Prostate Segmentation Methodologies in Ultrasound, Magnetic Resonance and Computed Tomography Images, Computer Methods and Programs in Biomedicine, Elsevier, hal-00695557, 2012.

Graham, V., Gwenael, G., Mike, B. (2012); Fully Automatic Segmentation of the Prostate using Active Appearance Models, PROMISE12 challenge website.

Heimann, T. et al. (2009); Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets, IEEE Transactions on Medical Imaging, 28(8): 1251-1265, DOI: 10.1109/TMI.2009.2013851. https://doi.org/10.1109/TMI.2009.2013851

Hemanth, D. J., Anitha, J., & Balas, V. E. (2015); Performance Improved Modified Fuzzy C-Means Algorithm for Image Segmentation Applications. Informatica, 26(4): 635-648. https://doi.org/10.15388/Informatica.2015.68

Kendall, D. (1989); A Survey of the Statistical Theory of Shape. Statistical Science, 4(2): 87-99. https://doi.org/10.1214/ss/1177012582

Klein, S., van der Heide,I., Lips, M., van Vulpen, M., Staring, M., Pluim, J. (2008); Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information; Med. Phys, 35(4):1407-1417. https://doi.org/10.1118/1.2842076

Lekas, R.et al. (2008); Monitoring changes in heart tissue temperature and evaluation of graft function after coronary artery bypass grafting surgery. Medicina, 45(3): 221-225.

Ling, H., Zhou, S.K., Zheng, Y., Georgescu, B., Suehling, M., Comaniciu, D. (2008); Hierarchical, learning-based automatic liver segmentation. CVPR, IEEE Computer Society, 1-8. https://doi.org/10.1109/cvpr.2008.4587393

Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., et al. (2014); Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge, Medical Image Analysis, 18(2):359-373. https://doi.org/10.1016/j.media.2013.12.002

Lorensen, W., Cline, H. (1987); Marching cubes: A high resolution 3d surface construction algorithm. SIGGRAPH Comput. Graph., 21(4): 163-169. https://doi.org/10.1145/37402.37422

Mottet, N., Bellmunt, J., Briers, E., Bergh, R., Bolla, M., Casteren, N., e. al. (2015); Guidelines on prostate cancer. European Association of Urology, 2015.

Perez, P., Gangnet, M., Blake, A. (2003); Poisson image editing, ACM Trans. Graph. 22(3): 313-318. https://doi.org/10.1145/882262.882269

Reddy, C. K., & Aggarwal, C. C. (Eds.). (2015); Healthcare data analytics, CRC Press, 2015.

Smailyt˙e, G., Aleknavicien˙e, B. (2015); V˙ežys Lietuvoje 2012 m. Nacionalinio v˙ežio instituto v˙ežio kontrol˙es ir profilaktikos centras, 2015.

Sylvain, A., Alain, C. (2010); A survey of cross-validation procedures for model selection. Statist. Surv, 4: 40-79. https://doi.org/10.1214/09-SS054

Termenon, M., Grana, M., Savio, A., Akusok, A., Miche, Y., Bjork, K. M., & Lendasse, A. (2016); Brain MRI morphological patterns extraction tool based on Extreme Learning Machine and majority vote classification. Neurocomputing, 174: 344-351. https://doi.org/10.1016/j.neucom.2015.03.111

Treigys, P., Šaltenis, V., Dzemyda, G., Barzdžiukas, V., & Paunksnis, A. (2008); Automated optic nerve disc parameterization, Informatica, 19(3): 403-420.

Trigui, R., Miteran, J., Sellami, L., Walker, P., & Hamida, A. B. (2016); A classification approach to prostate cancer localization in 3T multi-parametric MRI, Advanced Technologies for Signal and Image Processing (ATSIP), 2016 2nd International Conference on, IEEE, 113-118.

Vincent, G., Guillard, G., Bowes, M. (2012); Fully automatic segmentation of the prostate using active appearance models, MICCAI Grand Challenge: Prostate MR Image Segmentation, 2012.

Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D. (2008). Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imag. 27(11): 1668-1681. https://doi.org/10.1109/TMI.2008.2004421

Prostate Template Biopsy. Essexurology.co.uk. Retrieved 2016 July 30, 2016. from http://www.essexurology.co.uk/prostate_template_biopsy.php.

Published

2016-12-02

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