Accurate assessment of left atrial fibrosis in patients with atrial
fibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRI
images. However, obtaining such images is challenging due to patient motion,
changing breathing patterns, or sub-optimal choice of pulse sequence
parameters. Automated assessment of LGE-MRI image diagnostic quality is
clinically significant as it would enhance diagnostic accuracy, improve
efficiency, ensure standardization, and contributes to better patient outcomes
by providing reliable and high-quality LGE-MRI scans for fibrosis
quantification and treatment planning. To address this, we propose a two-stage
deep-learning approach for automated LGE-MRI image diagnostic quality
assessment. The method includes a left atrium detector to focus on relevant
regions and a deep network to evaluate diagnostic quality. We explore two
training strategies, multi-task learning, and pretraining using contrastive
learning, to overcome limited annotated data in medical imaging. Contrastive
Learning result shows about $4\%$, and $9\%$ improvement in F1-Score and
Specificity compared to Multi-Task learning when there's limited data.

自动化 LGE-MRI 图像诊断质量评估方法，包括左心房检测器和深度神经网络，通过多任务学习和对比学习提高精确度，用于左心房纤维化的可靠高质量扫描和治疗规划。

左心房晚期钆增强 MRI 图像质量评估的两阶段深度学习框架

Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial  Late Gadolinium Enhanced MRI Images

Segmentation of cardiac images, particularly late gadolinium-enhanced
magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased
cardiac structures, is a crucial first step for clinical diagnosis and
treatment. However, direct segmentation of LGE-MRIs is challenging due to its
attenuated contrast. Since most clinical studies have relied on manual and
labor-intensive approaches, automatic methods are of high interest,
particularly optimized machine learning approaches. To address this, we
organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs,
currently the world's largest cardiac LGE-MRI dataset, and associated labels of
the left atrium segmented by three medical experts, ultimately attracting the
participation of 27 international teams. In this paper, extensive analysis of
the submitted algorithms using technical and biological metrics was performed
by undergoing subgroup analysis and conducting hyper-parameter analysis,
offering an overall picture of the major design choices of convolutional neural
networks (CNNs) and practical considerations for achieving state-of-the-art
left atrium segmentation. Results show the top method achieved a dice score of
93.2% and a mean surface to a surface distance of 0.7 mm, significantly
outperforming prior state-of-the-art. Particularly, our analysis demonstrated
that double, sequentially used CNNs, in which a first CNN is used for automatic
region-of-interest localization and a subsequent CNN is used for refined
regional segmentation, achieved far superior results than traditional methods
and pipelines containing single CNNs. This large-scale benchmarking study makes
a significant step towards much-improved segmentation methods for cardiac
LGE-MRIs, and will serve as an important benchmark for evaluating and comparing
the future works in the field.

本研究基于全球最大的心脏 LGE-MRI 数据集，通过多种技术和生物学度量对 27 个国际团队的左房分割算法进行了深入分析，结果显示使用双连续 CNN 网络的方法明显优于传统方法和包含单个 CNN 的流程，达到了 93.2％的 Dice 分数和 0.7 毫米的平均表面距离，为心脏 LGE-MRI 分割方法的改进迈出了重要一步，并成为该领域评估和比较未来工作的重要基准。