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（DCE）心脏磁共振成像（CMRI）是诊断心肌血流（灌注）异常的常用模态。在典型的自由呼吸DCE-CMRI扫描中，会在不同的对比剂"流入/流出"阶段采集近300张心肌灌注的时间分辨图像。手动分割DCE图像序列中每个时间帧的心肌轮廓既繁琐又耗时，尤其在非刚性运动校正失败或不可用的情况下。虽然深度神经网络（DNN）在分析DCE-CMRI数据集方面展现出潜力，但缺乏一种可靠检测分割失败的"动态质量控制"（dQC）技术。本文提出了一种新的时空不确定性度量，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，并建立人类在环框架以改进分割结果。在所提方法中，我们将dQC工具检测到的前10%最不确定的分割结果交由人类专家进行修正。该方法显著提高了Dice得分（p<0.001），并将分割失败图像数量从16.2%降至11.3%，而随机选择相同数量分割结果进行人工修正的替代方法未取得显著改善。我们的结果表明，所提dQC框架能准确识别低质量分割，并可能实现动态CMRI数据集临床解读与报告中的高效基于DNN的人类在环分析流程。