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

Abstract

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of 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 the translation of 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 towards clinical translation. Recently, a number of uncertainty estimation methods have 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 2019-2020 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 percentages 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, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at <a href='https://github.com/RagMeh11/QU-BraTS'>https://github.com/RagMeh11/QU-BraTS</a>

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

“…The DL method can be correlated with the functioning of the neural network in the brain. In comparison to radiomic methods, precise tumor boundary annotation is not required with DL and thus saves a lot of time and human effort [ 20 – 24 ]. Furthermore, DL method takes into consideration the microenvironment of the surrounding lung parenchyma and can extract features that are adaptive to specific clinical outcomes, whereas radiomics can only describe general features which lacks specificity for outcome prediction [ 25 – 29 ].…”

Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas

“…The volumetric tool used to perform the tumor segmentation in this manuscript performed well on the BraTS dataset, the co-winners of the 2016 BraTS competition [ 8 ]. The BraTS dataset is a benchmark that researchers use to validate their methodologies in a controlled setting [ 8 , 10 , 21 ]. The metrics that are important for the potential implementation of volumetric tools in clinical and research settings include accuracy, reproducibility, efficiency, and the ability to predict outcomes.…”