Boran Sekeroglu1, Rahib Abiyev1, Ahmet Ilhan2. 1. Computer Engineering Department, Near East University, 99138, Nicosia, Cyprus, TR, Mersin 10, Turkey. 2. Computer Engineering Department, Near East University, 99138, Nicosia, Cyprus, TR, Mersin 10, Turkey. ahmet.ilhan@neu.edu.tr.
Abstract
PURPOSE: Segmentation is one of the critical steps in analyzing medical images since it provides meaningful information for the diagnosis, monitoring, and treatment of brain tumors. In recent years, several artificial intelligence-based systems have been developed to perform this task accurately. However, the unobtrusive or low-contrast occurrence of some tumors and similarities to healthy brain tissues make the segmentation task challenging. These yielded researchers to develop new methods for preprocessing the images and improving their segmentation abilities. METHODS: This study proposes an efficient system for the segmentation of the complete brain tumors from MRI images based on tumor localization and enhancement methods with a deep learning architecture named U-net. Initially, the histogram-based nonparametric tumor localization method is applied to localize the tumorous regions and the proposed tumor enhancement method is used to modify the localized regions to increase the visual appearance of indistinct or low-contrast tumors. The resultant images are fed to the original U-net architecture to segment the complete brain tumors. RESULTS: The performance of the proposed tumor localization and enhancement methods with the U-net is tested on benchmark datasets, BRATS 2012, BRATS 2019, and BRATS 2020, and achieved superior results as 0.94, 0.85, 0.87, 0.88 dice scores for the BRATS 2012 HGG-LGG, BRATS 2019, and BRATS 2020 datasets, respectively. CONCLUSION: The results and comparisons showed how the proposed methods improve the segmentation ability of the deep learning models and provide high-accuracy and low-cost segmentation of complete brain tumors in MRI images. The results might yield the implementation of the proposed methods in segmentation tasks of different medical fields.
PURPOSE: Segmentation is one of the critical steps in analyzing medical images since it provides meaningful information for the diagnosis, monitoring, and treatment of brain tumors. In recent years, several artificial intelligence-based systems have been developed to perform this task accurately. However, the unobtrusive or low-contrast occurrence of some tumors and similarities to healthy brain tissues make the segmentation task challenging. These yielded researchers to develop new methods for preprocessing the images and improving their segmentation abilities. METHODS: This study proposes an efficient system for the segmentation of the complete brain tumors from MRI images based on tumor localization and enhancement methods with a deep learning architecture named U-net. Initially, the histogram-based nonparametric tumor localization method is applied to localize the tumorous regions and the proposed tumor enhancement method is used to modify the localized regions to increase the visual appearance of indistinct or low-contrast tumors. The resultant images are fed to the original U-net architecture to segment the complete brain tumors. RESULTS: The performance of the proposed tumor localization and enhancement methods with the U-net is tested on benchmark datasets, BRATS 2012, BRATS 2019, and BRATS 2020, and achieved superior results as 0.94, 0.85, 0.87, 0.88 dice scores for the BRATS 2012 HGG-LGG, BRATS 2019, and BRATS 2020 datasets, respectively. CONCLUSION: The results and comparisons showed how the proposed methods improve the segmentation ability of the deep learning models and provide high-accuracy and low-cost segmentation of complete brain tumors in MRI images. The results might yield the implementation of the proposed methods in segmentation tasks of different medical fields.
Authors: Ying-Hwey Nai; Bernice W Teo; Nadya L Tan; Sophie O'Doherty; Mary C Stephenson; Yee Liang Thian; Edmund Chiong; Anthonin Reilhac Journal: Comput Biol Med Date: 2021-05-15 Impact factor: 4.589
Authors: Bjoern H Menze; Andras Jakab; Stefan Bauer; Jayashree Kalpathy-Cramer; Keyvan Farahani; Justin Kirby; Yuliya Burren; Nicole Porz; Johannes Slotboom; Roland Wiest; Levente Lanczi; Elizabeth Gerstner; Marc-André Weber; Tal Arbel; Brian B Avants; Nicholas Ayache; Patricia Buendia; D Louis Collins; Nicolas Cordier; Jason J Corso; Antonio Criminisi; Tilak Das; Hervé Delingette; Çağatay Demiralp; Christopher R Durst; Michel Dojat; Senan Doyle; Joana Festa; Florence Forbes; Ezequiel Geremia; Ben Glocker; Polina Golland; Xiaotao Guo; Andac Hamamci; Khan M Iftekharuddin; Raj Jena; Nigel M John; Ender Konukoglu; Danial Lashkari; José Antonió Mariz; Raphael Meier; Sérgio Pereira; Doina Precup; Stephen J Price; Tammy Riklin Raviv; Syed M S Reza; Michael Ryan; Duygu Sarikaya; Lawrence Schwartz; Hoo-Chang Shin; Jamie Shotton; Carlos A Silva; Nuno Sousa; Nagesh K Subbanna; Gabor Szekely; Thomas J Taylor; Owen M Thomas; Nicholas J Tustison; Gozde Unal; Flor Vasseur; Max Wintermark; Dong Hye Ye; Liang Zhao; Binsheng Zhao; Darko Zikic; Marcel Prastawa; Mauricio Reyes; Koen Van Leemput Journal: IEEE Trans Med Imaging Date: 2014-12-04 Impact factor: 10.048
Authors: Ramy A Zeineldin; Mohamed E Karar; Ziad Elshaer; Jan Coburger; Christian R Wirtz; Oliver Burgert; Franziska Mathis-Ullrich Journal: Int J Comput Assist Radiol Surg Date: 2022-04-23 Impact factor: 3.421