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[1811.02629] Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"> <head> <title>[1811.02629] Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20240822" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/1811.02629"/> <meta name="description" content="Abstract page for arXiv paper 1811.02629: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge" /> <meta property="og:url" content="https://arxiv.org/abs/1811.02629v3" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="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 challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Identifying the Best Machine Learning Algorithms for Brain Tumor..."/> <meta name="twitter:description" content="Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge" /><meta name="citation_author" content="Bakas, Spyridon" /><meta name="citation_author" content="Reyes, Mauricio" /><meta name="citation_author" content="Jakab, Andras" /><meta name="citation_author" content="Bauer, Stefan" /><meta name="citation_author" content="Rempfler, Markus" /><meta name="citation_author" content="Crimi, Alessandro" /><meta name="citation_author" content="Shinohara, Russell Takeshi" /><meta name="citation_author" content="Berger, Christoph" /><meta name="citation_author" content="Ha, Sung Min" /><meta name="citation_author" content="Rozycki, Martin" /><meta name="citation_author" content="Prastawa, Marcel" /><meta name="citation_author" content="Alberts, Esther" /><meta name="citation_author" content="Lipkova, Jana" /><meta name="citation_author" content="Freymann, John" /><meta name="citation_author" content="Kirby, Justin" /><meta name="citation_author" content="Bilello, Michel" /><meta name="citation_author" content="Fathallah-Shaykh, Hassan" /><meta name="citation_author" content="Wiest, Roland" /><meta name="citation_author" content="Kirschke, Jan" /><meta name="citation_author" content="Wiestler, Benedikt" /><meta name="citation_author" content="Colen, Rivka" /><meta name="citation_author" content="Kotrotsou, Aikaterini" /><meta name="citation_author" content="Lamontagne, Pamela" /><meta name="citation_author" content="Marcus, Daniel" /><meta name="citation_author" content="Milchenko, Mikhail" /><meta name="citation_author" content="Nazeri, Arash" /><meta name="citation_author" content="Weber, Marc-Andre" /><meta name="citation_author" content="Mahajan, Abhishek" /><meta name="citation_author" content="Baid, Ujjwal" /><meta name="citation_author" content="Gerstner, Elizabeth" /><meta name="citation_author" content="Kwon, Dongjin" /><meta name="citation_author" content="Acharya, Gagan" /><meta name="citation_author" content="Agarwal, Manu" /><meta name="citation_author" content="Alam, Mahbubul" /><meta name="citation_author" content="Albiol, Alberto" /><meta name="citation_author" content="Albiol, Antonio" /><meta name="citation_author" content="Albiol, Francisco J." /><meta name="citation_author" content="Alex, Varghese" /><meta name="citation_author" content="Allinson, Nigel" /><meta name="citation_author" content="Amorim, Pedro H. 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A." /><meta name="citation_author" content="Vidyaratne, Lasitha" /><meta name="citation_author" content="Vilaplana, Veronica" /><meta name="citation_author" content="Vivekanandan, Ajeet" /><meta name="citation_author" content="Wang, Guotai" /><meta name="citation_author" content="Wang, Qian" /><meta name="citation_author" content="Wang, Chiatse J." /><meta name="citation_author" content="Wang, Weichung" /><meta name="citation_author" content="Wang, Duo" /><meta name="citation_author" content="Wang, Ruixuan" /><meta name="citation_author" content="Wang, Yuanyuan" /><meta name="citation_author" content="Wang, Chunliang" /><meta name="citation_author" content="Wang, Guotai" /><meta name="citation_author" content="Wen, Ning" /><meta name="citation_author" content="Wen, Xin" /><meta name="citation_author" content="Weninger, Leon" /><meta name="citation_author" content="Wick, Wolfgang" /><meta name="citation_author" content="Wu, Shaocheng" /><meta name="citation_author" content="Wu, Qiang" /><meta name="citation_author" content="Wu, Yihong" /><meta name="citation_author" content="Xia, Yong" /><meta name="citation_author" content="Xu, Yanwu" /><meta name="citation_author" content="Xu, Xiaowen" /><meta name="citation_author" content="Xu, Peiyuan" /><meta name="citation_author" content="Yang, Tsai-Ling" /><meta name="citation_author" content="Yang, Xiaoping" /><meta name="citation_author" content="Yang, Hao-Yu" /><meta name="citation_author" content="Yang, Junlin" /><meta name="citation_author" content="Yang, Haojin" /><meta name="citation_author" content="Yang, Guang" /><meta name="citation_author" content="Yao, Hongdou" /><meta name="citation_author" content="Ye, Xujiong" /><meta name="citation_author" content="Yin, Changchang" /><meta name="citation_author" content="Young-Moxon, Brett" /><meta name="citation_author" content="Yu, Jinhua" /><meta name="citation_author" content="Yue, Xiangyu" /><meta name="citation_author" content="Zhang, Songtao" /><meta name="citation_author" content="Zhang, Angela" /><meta name="citation_author" content="Zhang, Kun" /><meta name="citation_author" content="Zhang, Xuejie" /><meta name="citation_author" content="Zhang, Lichi" /><meta name="citation_author" content="Zhang, Xiaoyue" /><meta name="citation_author" content="Zhang, Yazhuo" /><meta name="citation_author" content="Zhang, Lei" /><meta name="citation_author" content="Zhang, Jianguo" /><meta name="citation_author" content="Zhang, Xiang" /><meta name="citation_author" content="Zhang, Tianhao" /><meta name="citation_author" content="Zhao, Sicheng" /><meta name="citation_author" content="Zhao, Yu" /><meta name="citation_author" content="Zhao, Xiaomei" /><meta name="citation_author" content="Zhao, Liang" /><meta name="citation_author" content="Zheng, Yefeng" /><meta name="citation_author" content="Zhong, Liming" /><meta name="citation_author" content="Zhou, Chenhong" /><meta name="citation_author" content="Zhou, Xiaobing" /><meta name="citation_author" content="Zhou, Fan" /><meta name="citation_author" content="Zhu, Hongtu" /><meta name="citation_author" content="Zhu, Jin" /><meta name="citation_author" content="Zhuge, Ying" /><meta name="citation_author" content="Zong, Weiwei" /><meta name="citation_author" content="Kalpathy-Cramer, Jayashree" /><meta name="citation_author" content="Farahani, Keyvan" /><meta name="citation_author" content="Davatzikos, Christos" /><meta name="citation_author" content="van Leemput, Koen" /><meta name="citation_author" content="Menze, Bjoern" /><meta name="citation_date" content="2018/11/05" /><meta name="citation_online_date" content="2019/04/23" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/1811.02629" /><meta name="citation_arxiv_id" content="1811.02629" /><meta name="citation_abstract" content="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 challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a> <!-- start desktop header --> <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" 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trackback:ping="/trackback/1811.02629" /> </rdf:RDF> --><div id="abs-outer"> <div class="leftcolumn"> <div class="subheader"> <h1>Computer Science > Computer Vision and Pattern Recognition</h1> </div> <div class="header-breadcrumbs-mobile"> <strong>arXiv:1811.02629</strong> (cs) </div> <link rel="stylesheet" type="text/css" href="/static/base/1.0.1/css/abs.css"> <div id="content-inner"> <div id="abs"> <div class="dateline"> [Submitted on 5 Nov 2018 (<a href="https://arxiv.org/abs/1811.02629v1">v1</a>), last revised 23 Apr 2019 (this version, v3)]</div> <h1 class="title mathjax"><span class="descriptor">Title:</span>Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&query=Bakas,+S" rel="nofollow">Spyridon Bakas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Reyes,+M" rel="nofollow">Mauricio Reyes</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jakab,+A" rel="nofollow">Andras Jakab</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bauer,+S" rel="nofollow">Stefan Bauer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rempfler,+M" rel="nofollow">Markus Rempfler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Crimi,+A" rel="nofollow">Alessandro Crimi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shinohara,+R+T" rel="nofollow">Russell Takeshi Shinohara</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Berger,+C" rel="nofollow">Christoph Berger</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ha,+S+M" rel="nofollow">Sung Min Ha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rozycki,+M" rel="nofollow">Martin Rozycki</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Prastawa,+M" rel="nofollow">Marcel Prastawa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alberts,+E" rel="nofollow">Esther Alberts</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lipkova,+J" rel="nofollow">Jana Lipkova</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Freymann,+J" rel="nofollow">John Freymann</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kirby,+J" rel="nofollow">Justin Kirby</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bilello,+M" rel="nofollow">Michel Bilello</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fathallah-Shaykh,+H" rel="nofollow">Hassan Fathallah-Shaykh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wiest,+R" rel="nofollow">Roland Wiest</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kirschke,+J" rel="nofollow">Jan Kirschke</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wiestler,+B" rel="nofollow">Benedikt Wiestler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Colen,+R" rel="nofollow">Rivka Colen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kotrotsou,+A" rel="nofollow">Aikaterini Kotrotsou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lamontagne,+P" rel="nofollow">Pamela Lamontagne</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Marcus,+D" rel="nofollow">Daniel Marcus</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Milchenko,+M" rel="nofollow">Mikhail Milchenko</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nazeri,+A" rel="nofollow">Arash Nazeri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Weber,+M" rel="nofollow">Marc-Andre Weber</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mahajan,+A" rel="nofollow">Abhishek Mahajan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Baid,+U" rel="nofollow">Ujjwal Baid</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gerstner,+E" rel="nofollow">Elizabeth Gerstner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kwon,+D" rel="nofollow">Dongjin Kwon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Acharya,+G" rel="nofollow">Gagan Acharya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Agarwal,+M" rel="nofollow">Manu Agarwal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alam,+M" rel="nofollow">Mahbubul Alam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Albiol,+A" rel="nofollow">Alberto Albiol</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Albiol,+A" rel="nofollow">Antonio Albiol</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Albiol,+F+J" rel="nofollow">Francisco J. Albiol</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alex,+V" rel="nofollow">Varghese Alex</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Allinson,+N" rel="nofollow">Nigel Allinson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Amorim,+P+H+A" rel="nofollow">Pedro H. A. Amorim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Amrutkar,+A" rel="nofollow">Abhijit Amrutkar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Anand,+G" rel="nofollow">Ganesh Anand</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Andermatt,+S" rel="nofollow">Simon Andermatt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Arbel,+T" rel="nofollow">Tal Arbel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Arbelaez,+P" rel="nofollow">Pablo Arbelaez</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Avery,+A" rel="nofollow">Aaron Avery</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Azmat,+M" rel="nofollow">Muneeza Azmat</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=B.,+P" rel="nofollow">Pranjal B.</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bai,+W" rel="nofollow">W Bai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Banerjee,+S" rel="nofollow">Subhashis Banerjee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barth,+B" rel="nofollow">Bill Barth</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Batchelder,+T" rel="nofollow">Thomas Batchelder</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Batmanghelich,+K" rel="nofollow">Kayhan Batmanghelich</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Battistella,+E" rel="nofollow">Enzo Battistella</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Beers,+A" rel="nofollow">Andrew Beers</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Belyaev,+M" rel="nofollow">Mikhail Belyaev</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bendszus,+M" rel="nofollow">Martin Bendszus</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benson,+E" rel="nofollow">Eze Benson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bernal,+J" rel="nofollow">Jose Bernal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bharath,+H+N" rel="nofollow">Halandur Nagaraja Bharath</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Biros,+G" rel="nofollow">George Biros</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bisdas,+S" rel="nofollow">Sotirios Bisdas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Brown,+J" rel="nofollow">James Brown</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cabezas,+M" rel="nofollow">Mariano Cabezas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cao,+S" rel="nofollow">Shilei Cao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cardoso,+J+M" rel="nofollow">Jorge M. Cardoso</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Carver,+E+N" rel="nofollow">Eric N Carver</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Casamitjana,+A" rel="nofollow">Adri脿 Casamitjana</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Castillo,+L+S" rel="nofollow">Laura Silvana Castillo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cat%C3%A0,+M" rel="nofollow">Marcel Cat脿</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cattin,+P" rel="nofollow">Philippe Cattin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cerigues,+A" rel="nofollow">Albert Cerigues</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chagas,+V+S" rel="nofollow">Vinicius S. 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Manjunath</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Marcinkiewicz,+M" rel="nofollow">Michal Marcinkiewicz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=McDonagh,+S" rel="nofollow">S McDonagh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=McKenna,+S" rel="nofollow">Stephen McKenna</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=McKinley,+R" rel="nofollow">Richard McKinley</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mehl,+M" rel="nofollow">Miriam Mehl</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mehta,+S" rel="nofollow">Sachin Mehta</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mehta,+R" rel="nofollow">Raghav Mehta</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Meier,+R" rel="nofollow">Raphael Meier</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Meinel,+C" rel="nofollow">Christoph Meinel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Merhof,+D" rel="nofollow">Dorit Merhof</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Meyer,+C" rel="nofollow">Craig Meyer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Miller,+R" rel="nofollow">Robert Miller</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mitra,+S" rel="nofollow">Sushmita Mitra</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Moiyadi,+A" rel="nofollow">Aliasgar Moiyadi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Molina-Garcia,+D" rel="nofollow">David Molina-Garcia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Monteiro,+M+A" rel="nofollow">Miguel A.B. 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<a href="https://arxiv.org/search/cs?searchtype=author&query=Menze,+B" rel="nofollow">Bjoern Menze</a> </div> <a href="javascript:toggleAuthorList('long-author-list','et al. (327 additional authors not shown)');" title="Show Entire Author List" id="toggle"> et al. (327 additional authors not shown)</a> <noscript> You must enable JavaScript to view entire author list.</noscript></div> <div id="download-button-info" hidden>View a PDF of the paper titled Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, by Spyridon Bakas and 426 other authors</div> <a class="mobile-submission-download" href="/pdf/1811.02629">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>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 challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. </blockquote> <!--CONTEXT--> <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">The International Multimodal Brain Tumor Segmentation (BraTS) Challenge</td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Computer Vision and Pattern Recognition (cs.CV)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/1811.02629">arXiv:1811.02629</a> [cs.CV]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/1811.02629v3">arXiv:1811.02629v3</a> [cs.CV]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.1811.02629" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.1811.02629</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 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