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BibTex format

@article{Mehta:2022,
author = {Mehta, R and Filos, A and Baid, U and Sako, C and McKinley, R and Rebsamen, M and Dätwyler, K and Meier, R and Radojewski, P and Murugesan, GK and Nalawade, S and Ganesh, C and Wagner, B and Yu, FF and Fei, B and Madhuranthakam, AJ and Maldjian, JA and Daza, L and Gómez, C and Arbeláez, P and Dai, C and Wang, S and Reynaud, H and Mo, Y and Angelini, E and Guo, Y and Bai, W and Banerjee, S and Pei, L and Ak, M and Rosas-González, S and Zemmoura, I and Tauber, C and Vu, MH and Nyholm, T and Löfstedt, T and Ballestar, LM and Vilaplana, V and McHugh, H and Maso, Talou G and Wang, A and Patel, J and Chang, K and Hoebel, K and Gidwani, M and Arun, N and Gupta, S and Aggarwal, M and Singh, P and Gerstner, ER and Kalpathy-Cramer, J and Boutry, N and Huard, A and Vidyaratne, L and Rahman, MM and Iftekharuddin, KM and Chazalon, J and Puybareau, E and Tochon, G and Ma, J and Cabezas, M and Llado, X and Oliver, A and Valencia, L and Valverde, S and Amian, M and Soltaninejad, M and Myronenko, A and},
journal = {J Mach Learn Biomed Imaging},
title = {QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.},
url = {https://www.ncbi.nlm.nih.gov/pubmed/36998700},
volume = {2022},
year = {2022}
}
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TY  - JOUR
AB  - Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.
AU  - Mehta,R
AU  - Filos,A
AU  - Baid,U
AU  - Sako,C
AU  - McKinley,R
AU  - Rebsamen,M
AU  - Dätwyler,K
AU  - Meier,R
AU  - Radojewski,P
AU  - Murugesan,GK
AU  - Nalawade,S
AU  - Ganesh,C
AU  - Wagner,B
AU  - Yu,FF
AU  - Fei,B
AU  - Madhuranthakam,AJ
AU  - Maldjian,JA
AU  - Daza,L
AU  - Gómez,C
AU  - Arbeláez,P
AU  - Dai,C
AU  - Wang,S
AU  - Reynaud,H
AU  - Mo,Y
AU  - Angelini,E
AU  - Guo,Y
AU  - Bai,W
AU  - Banerjee,S
AU  - Pei,L
AU  - Ak,M
AU  - Rosas-González,S
AU  - Zemmoura,I
AU  - Tauber,C
AU  - Vu,MH
AU  - Nyholm,T
AU  - Löfstedt,T
AU  - Ballestar,LM
AU  - Vilaplana,V
AU  - McHugh,H
AU  - Maso,Talou G
AU  - Wang,A
AU  - Patel,J
AU  - Chang,K
AU  - Hoebel,K
AU  - Gidwani,M
AU  - Arun,N
AU  - Gupta,S
AU  - Aggarwal,M
AU  - Singh,P
AU  - Gerstner,ER
AU  - Kalpathy-Cramer,J
AU  - Boutry,N
AU  - Huard,A
AU  - Vidyaratne,L
AU  - Rahman,MM
AU  - Iftekharuddin,KM
AU  - Chazalon,J
AU  - Puybareau,E
AU  - Tochon,G
AU  - Ma,J
AU  - Cabezas,M
AU  - Llado,X
AU  - Oliver,A
AU  - Valencia,L
AU  - Valverde,S
AU  - Amian,M
AU  - Soltaninejad,M
AU  - Myronenko,A
AU  - Hatamizadeh,A
AU  - Feng,X
AU  - Dou,Q
AU  - Tustison,N
AU  - Meyer,C
AU  - Shah,NA
AU  - Talbar,S
AU  - Weber,M-A
AU  - Mahajan,A
AU  - Jakab,A
AU  - Wiest,R
AU  - Fathallah-Shaykh,HM
AU  - Nazeri,A
AU  - Milchenko,M
AU  - Marcus,D
AU  - Kotrotsou,A
AU  - Colen,R
AU  - Freymann,J
AU  - Kirby,J
AU  - Davatzikos,C
AU  - Menze,B
AU  - Bakas,S
AU  - Gal,Y
AU  - Arbel,T
PY  - 2022///
TI  - QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.
T2  - J Mach Learn Biomed Imaging
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36998700
VL  - 2022
ER  -
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