• Corpus ID: 245334392

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results

@article{Mehta2021QUBraTSMB,
  title={QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results},
  author={Raghav Mehta and Angelos Filos and Ujjwal Baid and Chiharu Sako and Richard McKinley and Michael Rebsamen and K. Datwyler and Raphael Meier and P. Radojewski and Gowtham Krishnan Murugesan and S. Nalawade and Chandan Ganesh and Benjamin C. Wagner and Fang Frank Yu and Baowei Fei and Ananth J. Madhuranthakam and Joseph A. Maldjian and Laura Alexandra Daza and Catalina G'omez and Pablo Arbel'aez and Chengliang Dai and Shuo Wang and Hadrien Raynaud and Yuanhan Mo and Elsa D. Angelini and Yike Guo and Wenjia Bai and Subhashis Banerjee and Linmin Pei and A Murat and Sarahi Rosas-Gonz'alez and Illyess Zemmoura and Clovis Tauber and Minh H. Vu and Tufve Nyholm and Tommy Lofstedt and Laura Mora Ballestar and Ver{\'o}nica Vilaplana and Hugh McHugh and Gonzalo D. Maso Talou and Alan Wang and Jay B. Patel and Ken Chang and Katharina Hoebel and Mishka Gidwani and Nishanth Thumbavanam Arun and Sharut Gupta and Mehak Aggarwal and Praveer Singh and Elizabeth R. Gerstner and Jayashree Kalpathy-Cramer and Nicolas Boutry and Alexis Huard and Lasitha S. Vidyaratne and Md Monibor Rahman and Khan M. Iftekharuddin and Joseph Chazalon and {\'E}lodie Puybareau and Guillaume Tochon and Jun Ma and Mariano Cabezas and Xavier Llad{\'o} and Arnau Oliver and Liliana Valencia and Sergi Valverde and Mehdi Amian and Mohammadreza Soltaninejad and Andriy Myronenko and Ali Hatamizadeh and Xuejing Feng and Quan Dou and N. Tustison and Craig Meyer and Nisarg A. Shah and Sanjay N. Talbar and M. A. Weber and Abhishek Mahajan and Andr{\'a}s Jakab and Roland Wiest and Hassan M. Fathallah-Shaykh and Arash Nazeri and Mikhail Milchenko and Daniel Marcus and Aikaterini Kotrotsou and Rivka R. Colen and John B. Freymann and Justin S. Kirby and Christos Davatzikos and Bjoern H Menze and Spyridon Bakas and Yarin Gal and Tal Arbel},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.10074}
}
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… 

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