@Misc{SpyridonBakas2018,
author="Spyridon Bakas
and Mauricio Reyes
and Andras Jakab
and Stefan Bauer
and Markus Rempfler
and Alessandro Crimi
and Russell Takeshi Shinohara
and Christoph Berger
and Sung Min Ha
and Martin Rozycki
and Marcel Prastawa
and Esther Alberts
and Jana Lipkova
and John Freymann
and Justin Kirby
and Michel Bilello
and Hassan Fathallah-Shaykh
and Roland Wiest
and Jan Kirschke
and Benedikt Wiestler
and Rivka Colen
and Aikaterini Kotrotsou
and Pamela Lamontagne
and Daniel Marcus
and Mikhail Milchenko
and Arash Nazeri
and Marc-Andre Weber
and Abhishek Mahajan
and Ujjwal Baid
and Dongjin Kwon
and Manu Agarwal
and Mahbubul Alam
and Alberto Albiol
and Antonio Albiol
and Varghese Alex
and Tuan Anh Tran
and Tal Arbel
and Aaron Avery
and Subhashis Banerjee
and Thomas Batchelder
and Kayhan Batmanghelich
and Enzo Battistella
and Martin Bendszus
and Eze Benson
and Jose Bernal
and George Biros
and Mariano Cabezas
and Siddhartha Chandra
and Yi-Ju Chang
and Joseph Chazalon
and Shengcong Chen
and Wei Chen
and Jefferson Chen
and Kun Cheng
and Meinel Christoph
and Roger Chylla
and Albert Cl{\'e}rigues
and Anthony Costa
and Xiaomeng Cui
and Zhenzhen Dai
and Lutao Dai
and Eric Deutsch
and Changxing Ding
and Chao Dong
and Wojciech Dudzik
and Theo Estienne
and Hyung Eun Shin
and Richard Everson
and Jonathan Fabrizio
and Longwei Fang
and Xue Feng
and Lucas Fidon
and Naomi Fridman
and Huan Fu
and David Fuentes
and David G Gering
and Yaozong Gao
and Evan Gates
and Amir Gholami
and Mingming Gong
and Sandra Gonzalez-Villa
and J Gregory Pauloski
and Yuanfang Guan
and Sheng Guo
and Sudeep Gupta
and Meenakshi H Thakur
and Klaus H Maier-Hein
and Woo-Sup Han
and Huiguang He
and Aura Hernandez-Sabate
and Evelyn Herrmann
and Naveen Himthani
and Winston Hsu
and Cheyu Hsu
and Xiaojun Hu
and Xiaobin Hu
and Yan Hu
and Yifan Hu
and Rui Hua",
title="Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge",
year="2018",
optkeywords="BraTS",
optkeywords="challenge",
optkeywords="brain",
optkeywords="tumor",
optkeywords="segmentation",
optkeywords="machine learning",
optkeywords="glioma",
optkeywords="glioblastoma",
optkeywords="radiomics",
optkeywords="survival",
optkeywords="progression",
optkeywords="RECIST",
abstract="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 multiparametric 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 preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone 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.",
optnote="ADAS; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3252), last updated on Thu, 28 Jan 2021 10:31:08 +0100"
}