PDF(Pubmed)
Abstract：
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.
摘要：
动态对比增强（DCE）心脏磁共振成像（CMRI）是一种广泛用于诊断心肌血流（灌注）异常的方式。在典型的自由呼吸DCE-CMRI扫描中，在不同的对比“冲洗/冲洗”阶段获得了近300张时间分辨的心肌灌注图像。在DCE图像系列的每个时间帧中对心肌轮廓进行手动分割可能既繁琐又耗时，特别是当非刚性运动校正失败或不可用时。虽然深度神经网络(DNN)已经显示出分析DCE-CMRI数据集的前景，缺乏用于可靠检测失败分割的“动态质量控制”（dQC）技术。在这里，我们提出了一种新的时空不确定性度量作为dQC工具，用于基于DNN的自由呼吸DCE-CMRI数据集的分割，通过在外部数据集上验证所提出的度量并建立人在环框架来改善分割结果。在拟议的方法中，我们将我们的dQC工具检测到的最不确定的前10%的分段转介给人类专家进行细化。该方法导致Dice评分的显着增加（p<0.001），并且分割失败的图像数量显着减少（16.2％至11.3％），而随机选择相同数量的分割用于人类转诊的替代方法没有实现任何显着改善。我们的结果表明，所提出的dQC框架有可能准确识别质量较差的分割，并可能在人在环管道中对DCE-CMRI进行有效的基于DNN的分析，以用于动态CMRI数据集的临床解释和报告。
