QU-BraTS: MICCAI 2020 BraTS挑战赛关于量化脑肿瘤分割中的不确定性 - 排名分数和基准结果分析。
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.
发表日期：2022 Aug
作者：
Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F Yu, Baowei Fei, Ananth J Madhuranthakam, Joseph A Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel
来源：
BIOMEDICINE & PHARMACOTHERAPY
摘要：
深度学习（DL）模型在各种医学成像基准测试挑战中提供了最先进的性能，包括脑肿瘤分割（BraTS）挑战。然而，聚焦病理多分区分割（例如肿瘤和损伤子区域）的任务特别具有挑战性，潜在误差阻碍了将DL模型转化为临床工作流程。以不确定性的形式量化DL模型预测的可靠性，可以使临床对最不确定的区域进行审查，从而建立信任并为临床转化铺平道路。最近引入了几种不确定性估计方法用于DL医学图像分割任务。开发用于评估和比较不确定度度量性能的分数将有助于最终用户做出更明智的决策。在本研究中，我们探索并评估了在BraTS 2019和BraTS 2020任务中开发的一项不确定性量化分数（QU-BraTS），旨在评估和排名用于脑肿瘤多分区分割的不确定度估计。该分数（1）奖励产生正确断言的高置信度的不确定度估计和在不正确的断言中分配低置信度水平的不确定度估计，以及（2）惩罚会导致更高比例不自信的正确断言的不确定度度量。我们还对QU-BraTS 2020的14个独立参与团队生成的分割不确定性进行基准测试，这些团队都参与了主要的BraTS分割任务。总的来说，我们的研究证实了不确定度估计对于分割算法的重要性和互补价值，突显了医学图像分析中不确定性量化的必要性。最后，为了透明和可重复性，我们的评估代码公开发布在https://github.com/RagMeh11/QU-BraTS。
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.