@article{(Open Science Index):https://publications.waset.org/pdf/10012201,
	  title     = {Automated 3D Segmentation System for Detecting Tumor and Its Heterogeneity in Patients with High Grade Ovarian Epithelial Cancer},
	  author    = {D. A. Binas and  M. Konidari and  C. Bourgioti and  L. Angela Moulopoulou and  T. L. Economopoulos and  G. K. Matsopoulos},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {15},
	  number    = {9},
	  year      = {2021},
	  pages     = {272 - 275},
	  ee        = {https://publications.waset.org/pdf/10012201},
	  url   	= {https://publications.waset.org/vol/177},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 177, 2021},
	}