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


  • 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


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


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.


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