Automated 3D Segmentation System for Detecting Tumor and Its Heterogeneity in Patients with High Grade Ovarian Epithelial Cancer
High grade ovarian epithelial cancer (OEC) is the most fatal gynecological cancer and poor prognosis of this entity is closely related to considerable intratumoral genetic heterogeneity. By examining imaging data, it is possible to assess the heterogeneity of tumorous tissue. This study presents a methodology for aligning, segmenting and finally visualizing information from various magnetic resonance imaging series, in order to construct 3D models of heterogeneity maps from the same tumor in OEC patients. The proposed system may be used as an adjunct digital tool by health professionals for personalized medicine, as it allows for an easy visual assessment of the heterogeneity of the examined tumor.
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[1] S. A. Cannistra, “Cancer of the Ovary,” N. Engl. J. Med., vol. 351, no. 24, pp. 2519–2529, Dec. 2004, doi: 10.1056/NEJMra041842.
[2] D. D. Bowtell et al., “Rethinking ovarian cancer II: Reducing mortality from high-grade serous ovarian cancer,” Nature Reviews Cancer, vol. 15, no. 11. Nature Publishing Group, pp. 668–679, Oct. 23, 2015, doi: 10.1038/nrc4019.
[3] L. R. Medeiros et al., “Accuracy of magnetic resonance imaging in ovarian tumor: A systematic quantitative review,” Am. J. Obstet. Gynecol., vol. 204, no. 1, pp. 67.e1-67.e10, 2011, doi: 10.1016/j.ajog.2010.08.031.
[4] E. Sala, S. Wakely, E. Senior, and D. Lomas, “MRI of malignant neoplasms of the uterine corpus and cervix,” American Journal of Roentgenology, vol. 188, no. 6. AJR Am J Roentgenol, pp. 1577–1587, Jun. 2007, doi: 10.2214/AJR.06.1196.
[5] L. G. Brown, “A survey of image registration techniques,” ACM Comput. Surv., vol. 24, no. 4, pp. 325–376, Jan. 1992, doi: 10.1145/146370.146374.
[6] D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank, “PET-CT image registration in the chest using free-form deformations,” IEEE Trans. Med. Imaging, vol. 22, no. 1, pp. 120–128, Jan. 2003, doi: 10.1109/TMI.2003.809072.
[7] S. Raghunathan, P. Schmalbrock, and B. D. Clymer, “Image Registration Using Rigid Registration and Maximization of Mutual Information" in The 13th Annual Medicine Meets Virtual Reality Conference, 2005.
[8] J. P. Thirion, “Image matching as a diffusion process: An analogy with Maxwell’s demons,” Med. Image Anal., vol. 2, no. 3, pp. 243–260, Sep. 1998, doi: 10.1016/S1361-8415(98)80022-4.
[9] W. E. Higgins and E. J. Ojard, “Interactive morphological watershed analysis for 3D medical images,” Comput. Med. Imaging Graph., vol. 17, no. 4–5, pp. 387–395, 1993, doi: 10.1016/0895-6111(93)90033-J.
[10] T. Taxt, A. Lundervold, B. Fuglaas, H. Lien, and V. Abeler, “Multispectral analysis of uterine corpus tumors in magnetic resonance imaging,” Magn. Reson. Med., vol. 23, no. 1, pp. 55–76, Jan. 1992, doi: 10.1002/mrm.1910230108.
[11] “ITK | Insight Toolkit.” https://itk.org/ (accessed May 05, 2021).
[12] VTK - The Visualization Toolkit.” https://vtk.org/ (accessed May 05, 2021).
[13] A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, “Morphometric analysis of white matter lesions in MR images: method and validation.,” IEEE Trans. Med. Imaging, vol. 13, no. 4, pp. 716–24, 1994, doi: 10.1109/42.363096.
@article{"International Journal of Medical, Medicine and Health Sciences:80475", author = "D. A. Binas and M. Konidari and C. Bourgioti and L. Angela Moulopoulou and T. L. Economopoulos and G. K. Matsopoulos", title = "Automated 3D Segmentation System for Detecting Tumor and Its Heterogeneity in Patients with High Grade Ovarian Epithelial Cancer", abstract = "High grade ovarian epithelial cancer (OEC) is the most fatal gynecological cancer and poor prognosis of this entity is closely related to considerable intratumoral genetic heterogeneity. By examining imaging data, it is possible to assess the heterogeneity of tumorous tissue. This study presents a methodology for aligning, segmenting and finally visualizing information from various magnetic resonance imaging series, in order to construct 3D models of heterogeneity maps from the same tumor in OEC patients. The proposed system may be used as an adjunct digital tool by health professionals for personalized medicine, as it allows for an easy visual assessment of the heterogeneity of the examined tumor.", keywords = "K-means segmentation, ovarian epithelial cancer, quantitative characteristics, registration, tumor visualization.", volume = "15", number = "9", pages = "207-4", }
