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- Mehta, Raghav, et al.
(författare)
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QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
- 2022
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Ingår i: Journal of Machine Learning for Biomedical Imaging. - 2766-905X. ; , s. 1-54
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Tidskriftsartikel (refereegranskat)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 https://github.com/RagMeh11/QU-BraTS
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| 2. |
- Neth, Bryan J, et al.
(författare)
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Modified ketogenic diet is associated with improved cerebrospinal fluid biomarker profile, cerebral perfusion, and cerebral ketone body uptake in older adults at risk for Alzheimer's disease: a pilot study.
- 2020
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Ingår i: Neurobiology of aging. - : Elsevier BV. - 1558-1497 .- 0197-4580. ; 86, s. 54-63
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Tidskriftsartikel (refereegranskat)abstract
- There is currently no established therapy to treat or prevent Alzheimer's disease. The ketogenic diet supplies an alternative cerebral metabolic fuel, with potential neuroprotective effects. Our goal was to compare the effects of a modified Mediterranean-ketogenic diet (MMKD) and an American Heart Association Diet (AHAD) on cerebrospinal fluid Alzheimer's biomarkers, neuroimaging measures, peripheral metabolism, and cognition in older adults at risk for Alzheimer's. Twenty participants with subjective memory complaints (n= 11) or mild cognitive impairment (n= 9) completed both diets, with 3 participants discontinuing early. Mean compliance rates were 90% for MMKD and 95% for AHAD. All participants had improved metabolic indices following MMKD. MMKD was associated with increased cerebrospinal fluid Aβ42 and decreased tau. There was increased cerebral perfusion and increased cerebral ketone body uptake (11C-acetoacetate PET, in subsample) following MMKD. Memory performance improved after both diets, which may be due to practice effects. Our results suggest that a ketogenic intervention targeted toward adults at risk for Alzheimer's may prove beneficial in the prevention of cognitive decline.
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