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<!doctype html><html><head><title>View article</title><meta http-equiv="Content-Type" content="text/html;charset=UTF-8"><meta http-equiv="X-UA-Compatible" content="IE=Edge"><meta name="referrer" content="always"><meta name="viewport" content="width=device-width,initial-scale=1,minimum-scale=1,maximum-scale=2"><meta name="format-detection" content="telephone=no"><link rel="shortcut icon" href="/favicon.ico"><meta name="description" content="‪S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, C Berger, SM Ha, M Rozycki…‬, ‪arXiv preprint arXiv:1811.02629, 2018‬ - ‪Cited by 2,051‬"><meta property="og:description" content="‪S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, C Berger, SM Ha, M Rozycki…‬, ‪arXiv preprint arXiv:1811.02629, 2018‬ - ‪Cited by 2,051‬"><meta property="og:title" content="‪Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS 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0;color:#e2e2e2;text-decoration:underline;}#gs_alrt_m a:active{color:#f6aea9}</style><form action="" method="post" id="gs_alrt"><span id="gs_alrt_m"></span><span id="gs_alrt_h"></span><a id="gs_alrt_l" href="javascript:void(0)" class="gs_fm_s" data-fm="gs_alrt"></a></form><div id="gs_bdy"><div id="gs_bdy_sb" role="navigation"><div id="gs_bdy_sb_in"><div id="gsc_sb_ui" class="gs_bdy_sb_sec"><a href="/citations?user=0rZ39wYAAAAJ&hl=en" class="gs_ibl"><span class="gs_rimg gs_pp_sm"><img alt="Wei Chen" sizes="56px" src="/citations/images/avatar_scholar_56.png" srcset="/citations/images/avatar_scholar_56.png 56w,/citations/images/avatar_scholar_128.png 128w" width="56" height="56"></span></a><div><a href="/citations?user=0rZ39wYAAAAJ&hl=en">Wei Chen</a></div></div></div></div><div id="gs_bdy_ccl" role="main"><div id="gsc_vcpb" data-edit-link="/citations?view_op=edit_citation&hl=en&user=0rZ39wYAAAAJ&citation_for_view=0rZ39wYAAAAJ:XVBKMArtY_gC&citation_for_view=0rZ39wYAAAAJ:XVBKMArtY_gC&back_view_op=view_citation"><form method="post" id="gsc_vcpb_btns" action="/citations?view_op=view_citation&hl=en"><input type="hidden" name="xsrf" value="ADIE8skAAAAAZ0M64EUf_UcUaYETWazSMl36DCs"><input type="hidden" name="s" value="0rZ39wYAAAAJ:XVBKMArtY_gC"><input type="hidden" name="continue" value="/citations?view_op=list_works&hl=en&hl=en&user=0rZ39wYAAAAJ"></form><div id="gsc_oci_title_wrapper"><div id="gsc_oci_title_gg"><div class="gsc_oci_title_ggi"><a href="https://arxiv.org/pdf/1811.02629" data-clk="hl=en&sa=T&ei=YOlBZ-yLFqm-y9YP7_zFiAE"><span class='gsc_vcd_title_ggt'>[PDF]</span> from arxiv.org</a></div></div><div id="gsc_oci_title"><a class="gsc_oci_title_link" href="https://arxiv.org/abs/1811.02629" data-clk="hl=en&sa=T&ei=YOlBZ-yLFqm-y9YP7_zFiAE">Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge</a></div></div><div id="gsc_oci_table"><div class="gs_scl"><div class="gsc_oci_field">Authors</div><div class="gsc_oci_value">Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J Albiol, Varghese Alex, Nigel Allinson, Pedro HA Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S Jackson, Sachin R Jambawalikar</div></div><div class="gs_scl"><div class="gsc_oci_field">Publication date</div><div class="gsc_oci_value">2018/11/5</div></div><div class="gs_scl"><div class="gsc_oci_field">Journal</div><div class="gsc_oci_value">arXiv preprint arXiv:1811.02629</div></div><div class="gs_scl"><div class="gsc_oci_field">Description</div><div class="gsc_oci_value" id="gsc_oci_descr"><div class="gsh_small">Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the …</div></div></div><div class="gs_scl"><div class="gsc_oci_field">Total citations</div><div class="gsc_oci_value"><div style="margin-bottom:1em"><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5">Cited by 2051</a></div><div id="gsc_oci_graph_wrapper"><div id="gsc_oci_graph" style="width:237px;"><div id="gsc_oci_graph_x"></div><div id="gsc_oci_graph_bars"><span class="gsc_oci_g_t" style="left:0px">2018</span><span class="gsc_oci_g_t" style="left:33px">2019</span><span class="gsc_oci_g_t" style="left:66px">2020</span><span class="gsc_oci_g_t" style="left:99px">2021</span><span class="gsc_oci_g_t" style="left:132px">2022</span><span class="gsc_oci_g_t" style="left:165px">2023</span><span class="gsc_oci_g_t" style="left:198px">2024</span><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5&as_ylo=2018&as_yhi=2018" class="gsc_oci_g_a" style="left:5px;height:1px;top:56px;z-index:7"><span class="gsc_oci_g_al">6</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5&as_ylo=2019&as_yhi=2019" class="gsc_oci_g_a" style="left:38px;height:13px;top:44px;z-index:6"><span class="gsc_oci_g_al">101</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5&as_ylo=2020&as_yhi=2020" class="gsc_oci_g_a" style="left:71px;height:35px;top:22px;z-index:5"><span class="gsc_oci_g_al">280</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5&as_ylo=2021&as_yhi=2021" class="gsc_oci_g_a" style="left:104px;height:50px;top:7px;z-index:4"><span class="gsc_oci_g_al">396</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5&as_ylo=2022&as_yhi=2022" class="gsc_oci_g_a" style="left:137px;height:48px;top:9px;z-index:3"><span class="gsc_oci_g_al">384</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5&as_ylo=2023&as_yhi=2023" class="gsc_oci_g_a" style="left:170px;height:57px;top:0px;z-index:2"><span class="gsc_oci_g_al">454</span></a><a href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5&as_ylo=2024&as_yhi=2024" class="gsc_oci_g_a" style="left:203px;height:51px;top:6px;z-index:1"><span class="gsc_oci_g_al">405</span></a></div></div></div></div></div><div class="gs_scl"><div class="gsc_oci_field">Scholar articles</div><div class="gsc_oci_value"><div class="gsc_oci_merged_snippet"><div><a href="/scholar?oi=bibs&cluster=7713865873909162489&btnI=1&hl=en">Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge</a></div><div>S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler… - arXiv preprint arXiv:1811.02629, 2018</div><div><a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cites=7713865873909162489&as_sdt=5">Cited by 2051</a> <a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&q=related:-SFdeyQoDWsJ:scholar.google.com/">Related articles</a> <a class="gsc_oms_link" href="https://scholar.google.co.in/scholar?oi=bibs&hl=en&cluster=7713865873909162489">All 9 versions</a> </div></div></div></div></div></div></div></div><div id="gs_ftr_sp" role="presentation"></div><div id="gs_ftr" class="gs_md_rmb" role="contentinfo"><div id="gs_ftr_rt"><a href="//www.google.co.in/intl/en/policies/privacy/">Privacy</a><a href="//www.google.co.in/intl/en/policies/terms/">Terms</a><a href="javascript:void(0)" ontouchstart="gs_evt_dsp(event)" role="button" aria-controls="gs_ftr_mnu" aria-haspopup="true" class="gs_press gs_md_tb">Help</a></div><div id="gs_ftr_mnu" class="gs_md_d gs_md_ds gs_ttzi gs_md_ulr" role="menu" tabindex="-1"><a role="menuitem" href="/intl/en/scholar/about.html" tabindex="-1" class="gs_md_li">About Scholar</a><a role="menuitem" href="//support.google.com/websearch?p=scholar_dsa&hl=en" tabindex="-1" class="gs_md_li">Search help</a></div></div></div></body></html>