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.

翻译：动态对比增强心脏磁共振成像（DCE-CMRI）是诊断心肌血流（灌注）异常的常用影像学手段。在典型的自由呼吸DCE-CMRI扫描中，需采集约300个时间分辨的心肌灌注图像，涵盖对比剂的不同"流入/流出"阶段。当非刚性运动校正失败或不可用时，手工分割DCE图像序列中每个时间帧的心肌轮廓既繁琐又耗时。尽管深度神经网络（DNN）在分析DCE-CMRI数据集方面展现出潜力，但缺乏用于可靠检测分割失败的"动态质量控制"（dQC）技术。本文提出一种新型时空不确定性度量，作为基于DNN的自由呼吸DCE-CMRI数据集分割的dQC工具，通过外部数据集验证该度量，并建立人机协同框架以改善分割结果。在所提出的方法中，我们将dQC工具检测出的最不确定的前10%分割结果交由人类专家修正。该方法显著提高了Dice评分（p<0.001），并将分割失败图像数量从16.2%降至11.3%，而随机选取相同数量分割结果进行人工修正的替代方案未取得显著改善。研究结果表明，本文提出的dQC框架能准确识别低质量分割，有望在动态CMRI数据集的临床判读与报告过程中，通过人机协同流水线实现高效的基于DNN的DCE-CMRI分析。