Imperial College London
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Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Bakas:2019,
author = {Bakas, S and Reyes, M and Jakab, A and Bauer, S and Rempfler, M and Crimi, A and Shinohara, RT and Berger, C and Ha, SM and Rozycki, M and Prastawa, M and Alberts, E and Lipkova, J and Freymann, J and Kirby, J and Bilello, M and Fathallah-Shaykh, H and Wiest, R and Kirschke, J and Wiestler, B and Colen, R and Kotrotsou, A and Lamontagne, P and Marcus, D and Milchenko, M and Nazeri, A and Weber, M-A and Mahajan, A and Baid, U and Gerstner, E and Kwon, D and Acharya, G and Agarwal, M and Alam, M and Albiol, A and Albiol, A and Albiol, FJ and Alex, V and Allinson, N and Amorim, PHA and Amrutkar, A and Anand, G and Andermatt, S and Arbel, T and Arbelaez, P and Avery, A and Azmat, M and Pranjal, B and Bai, W and Banerjee, S and Barth, B and Batchelder, T and Batmanghelich, K and Battistella, E and Beers, A and Belyaev, M and Bendszus, M and Benson, E and Bernal, J and Bharath, HN and Biros, G and Bisdas, S and Brown, J and Cabezas, M and Cao, S and Cardoso, JM and Carver, EN and Casamitjana},
title = {Identifying the best machine learning algorithms for brain tumorsegmentation, progression assessment, and overall survival prediction in the BRATS challenge},
url = {http://arxiv.org/abs/1811.02629v3},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Gliomas are the most common primary brain malignancies, with differentdegrees of aggressiveness, variable prognosis and various heterogeneoushistologic sub-regions, i.e., peritumoral edematous/invaded tissue, necroticcore, active and non-enhancing core. This intrinsic heterogeneity is alsoportrayed in their radio-phenotype, as their sub-regions are depicted byvarying intensity profiles disseminated across multi-parametric magneticresonance imaging (mpMRI) scans, reflecting varying biological properties.Their heterogeneous shape, extent, and location are some of the factors thatmake these tumors difficult to resect, and in some cases inoperable. The amountof resected tumor is a factor also considered in longitudinal scans, whenevaluating the apparent tumor for potential diagnosis of progression.Furthermore, there is mounting evidence that accurate segmentation of thevarious tumor sub-regions can offer the basis for quantitative image analysistowards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor imageanalysis in mpMRI scans, during the last seven instances of the InternationalBrain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, wefocus on i) evaluating segmentations of the various glioma sub-regions inpre-operative mpMRI scans, ii) assessing potential tumor progression by virtueof longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANOcriteria, and iii) predicting the overall survival from pre-operative mpMRIscans of patients that underwent gross total resection. Finally, we investigatethe 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 continuouslyevolving/growing dataset.
AU  - Bakas,S
AU  - Reyes,M
AU  - Jakab,A
AU  - Bauer,S
AU  - Rempfler,M
AU  - Crimi,A
AU  - Shinohara,RT
AU  - Berger,C
AU  - Ha,SM
AU  - Rozycki,M
AU  - Prastawa,M
AU  - Alberts,E
AU  - Lipkova,J
AU  - Freymann,J
AU  - Kirby,J
AU  - Bilello,M
AU  - Fathallah-Shaykh,H
AU  - Wiest,R
AU  - Kirschke,J
AU  - Wiestler,B
AU  - Colen,R
AU  - Kotrotsou,A
AU  - Lamontagne,P
AU  - Marcus,D
AU  - Milchenko,M
AU  - Nazeri,A
AU  - Weber,M-A
AU  - Mahajan,A
AU  - Baid,U
AU  - Gerstner,E
AU  - Kwon,D
AU  - Acharya,G
AU  - Agarwal,M
AU  - Alam,M
AU  - Albiol,A
AU  - Albiol,A
AU  - Albiol,FJ
AU  - Alex,V
AU  - Allinson,N
AU  - Amorim,PHA
AU  - Amrutkar,A
AU  - Anand,G
AU  - Andermatt,S
AU  - Arbel,T
AU  - Arbelaez,P
AU  - Avery,A
AU  - Azmat,M
AU  - Pranjal,B
AU  - Bai,W
AU  - Banerjee,S
AU  - Barth,B
AU  - Batchelder,T
AU  - Batmanghelich,K
AU  - Battistella,E
AU  - Beers,A
AU  - Belyaev,M
AU  - Bendszus,M
AU  - Benson,E
AU  - Bernal,J
AU  - Bharath,HN
AU  - Biros,G
AU  - Bisdas,S
AU  - Brown,J
AU  - Cabezas,M
AU  - Cao,S
AU  - Cardoso,JM
AU  - Carver,EN
AU  - Casamitjana,A
AU  - Castillo,LS
AU  - Catà,M
AU  - Cattin,P
AU  - Cerigues,A
AU  - Chagas,VS
AU  - Chandra,S
AU  - Chang,Y-J
AU  - Chang,S
AU  - Chang,K
AU  - Chazalon,J
AU  - Chen,S
AU  - Chen,W
AU  - Chen,JW
AU  - Chen,Z
AU  - Cheng,K
AU  - Choudhury,AR
AU  - Chylla,R
AU  - Clérigues,A
AU  - Colleman,S
AU  - Colmeiro,RGR
AU  - Combalia,M
AU  - Costa,A
AU  - Cui,X
AU  - Dai,Z
AU  - Dai,L
AU  - Daza,LA
AU  - Deutsch,E
AU  - Ding,C
AU  - Dong,C
AU  - Dong,S
AU  - Dudzik,W
AU  - Eaton-Rosen,Z
AU  - Egan,G
AU  - Escudero,G
AU  - Estienne,T
AU  - Everson,R
AU  - Fabrizio,J
AU  - Fan,Y
AU  - Fang,L
AU  - Feng,X
AU  - Ferrante,E
AU  - Fidon,L
AU  - Fischer,M
AU  - French,AP
AU  - Fridman,N
AU  - Fu,H
AU  - Fuentes,D
AU  - Gao,Y
AU  - Gates,E
AU  - Gering,D
AU  - Gholami,A
AU  - Gierke,W
AU  - Glocker,B
AU  - Gong,M
AU  - González-Villá,S
AU  - Grosges,T
AU  - Guan,Y
AU  - Guo,S
AU  - Gupta,S
AU  - Han,W-S
AU  - Han,IS
AU  - Harmuth,K
AU  - He,H
AU  - Hernández-Sabaté,A
AU  - Herrmann,E
AU  - Himthani,N
AU  - Hsu,W
AU  - Hsu,C
AU  - Hu,X
AU  - Hu,X
AU  - Hu,Y
AU  - Hu,Y
AU  - Hua,R
AU  - Huang,T-Y
AU  - Huang,W
AU  - Huffel,SV
AU  - Huo,Q
AU  - Vivek,HV
AU  - Iftekharuddin,KM
AU  - Isensee,F
AU  - Islam,M
AU  - Jackson,AS
AU  - Jambawalikar,SR
AU  - Jesson,A
AU  - Jian,W
AU  - Jin,P
AU  - Jose,VJM
AU  - Jungo,A
AU  - Kainz,B
AU  - Kamnitsas,K
AU  - Kao,P-Y
AU  - Karnawat,A
AU  - Kellermeier,T
AU  - Kermi,A
AU  - Keutzer,K
AU  - Khadir,MT
AU  - Khened,M
AU  - Kickingereder,P
AU  - Kim,G
AU  - King,N
AU  - Knapp,H
AU  - Knecht,U
AU  - Kohli,L
AU  - Kong,D
AU  - Kong,X
AU  - Koppers,S
AU  - Kori,A
AU  - Krishnamurthi,G
AU  - Krivov,E
AU  - Kumar,P
AU  - Kushibar,K
AU  - Lachinov,D
AU  - Lambrou,T
AU  - Lee,J
AU  - Lee,C
AU  - Lee,Y
AU  - Lee,M
AU  - Lefkovits,S
AU  - Lefkovits,L
AU  - Levitt,J
AU  - Li,T
AU  - Li,H
AU  - Li,W
AU  - Li,H
AU  - Li,X
AU  - Li,Y
AU  - Li,H
AU  - Li,Z
AU  - Li,X
AU  - Li,Z
AU  - Li
PY  - 2019///
TI  - Identifying the best machine learning algorithms for brain tumorsegmentation, progression assessment, and overall survival prediction in the BRATS challenge
UR  - http://arxiv.org/abs/1811.02629v3
UR  - http://hdl.handle.net/10044/1/71920
ER  -
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