42 research outputs found

    Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets

    Full text link
    Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p<0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.Comment: Accepted for publication in MICCAI 202

    Predicting the Effects of Supplemental EPA and DHA on the Omega-3 Index

    Get PDF
    Background: Supplemental long-chain omega-3 (n–3) fatty acids (EPA and DHA) raise erythrocyte EPA + DHA [omega-3 index (O3I)] concentrations, but the magnitude or variability of this effect is unclear. Objective: The purpose of this study was to model the effects of supplemental EPA + DHA on the O3I. Methods: Deidentified data from 1422 individuals from 14 published n–3 intervention trials were included. Variables considered included dose, baseline O3I, sex, age, weight, height, chemical form [ethyl ester (EE) compared with triglyceride (TG)], and duration of treatment. The O3I was measured by the same method in all included studies. Variables were selected by stepwise regression using the Bayesian information criterion. Results: Individuals supplemented with EPA + DHA (n = 846) took a mean ± SD of 1983 ± 1297 mg/d, and the placebo controls (n = 576) took none. The mean duration of supplementation was 13.6 ± 6.0 wk. The O3I increased from 4.9% ± 1.7% to 8.1% ± 2.7% in the supplemented individuals ( P \u3c 0.0001). The final model included dose, baseline O3I, and chemical formulation type (EE or TG), and these explained 62% of the variance in response (P \u3c 0.0001). The model predicted that the final O3I (and 95% CI) for a population like this, with a baseline concentration of 4.9%, given 850 mg/d of EPA + DHA EE would be ∌6.5% (95% CI: 6.3%, 6.7%). Gram for gram, TG-based supplements increased the O3I by about 1 percentage point more than EE products. Conclusions: Of the factors tested, only baseline O3I, dose, and chemical formulation were significant predictors of O3I response to supplementation. The model developed here can be used by researchers to help estimate the O3I response to a given EPA + DHA dose and chemical form
    corecore