Zhang Ze, Wang Zhen, Wang Xiqian, Wang Kuanquan, Yuan Yongfeng, Li Qince
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Department of Spinal Surgery, Zibo Central Hospital, Zibo, China.
Front Physiol. 2024 Dec 10;15:1478347. doi: 10.3389/fphys.2024.1478347. eCollection 2024.
Automatic segmentation of the left atrium (LA) constitutes a crucial pre-processing step in evaluating heart structure and function during clinical interventions, such as image-guided radiofrequency ablation of atrial fibrillation. Despite prior research on LA segmentation, the low contrast in medical images exacerbates the challenge of distinguishing various tissues, rendering accurate boundary delineation of the target area formidable. Moreover, class imbalance due to the small target size further complicates segmentation.
This study aims to devise an architecture that augments edge information for LA segmentation from late gadolinium enhancement magnetic resonance imaging. To intensify edge information within image features, this study introduces an Edge Information Enhancement Module (EIEM) to the foundational network. The design of EIEM is grounded in exploring edge details within target region features learned from images. Additionally, it incorporates a Spatially Weighted Cross-Entropy loss function tailored for EIEM, introducing constraints on different regions based on the importance of pixels to edge segmentation, while also mitigating class imbalance through weighted treatment of positive and negative samples.
The proposed method is validated on the 2018 Atrial Segmentation Challenge dataset. Compared with other state-of-the-art algorithms, the proposed algorithm demonstrated a significant improvement with an average symmetric surface distance of 0.684 mm and achieved a commendable Dice coefficient of 0.924, implicating the effectiveness of enhancing edge information.
The method offers a practical framework for precise LA localization and segmentation, particularly strengthening the algorithm's effectiveness in improving segmentation outcomes for irregular protrusions and discrete multiple targets. Additionally, the generalizability of our model was evaluated on the heart dataset from the Medical Segmentation Decathlon (MSD) challenge, confirming its robustness across different clinical scenarios involving LA segmentation.
左心房（LA）的自动分割是临床干预（如图像引导下的房颤射频消融）中评估心脏结构和功能的关键预处理步骤。尽管之前已有关于LA分割的研究，但医学图像中的低对比度加剧了区分各种组织的挑战，使得准确勾勒目标区域的边界变得困难重重。此外，由于目标尺寸较小导致的类别不平衡进一步使分割变得复杂。
本研究旨在设计一种架构，用于增强延迟钆增强磁共振成像中LA分割的边缘信息。为了强化图像特征中的边缘信息，本研究在基础网络中引入了边缘信息增强模块（EIEM）。EIEM的设计基于探索从图像中学习到的目标区域特征内的边缘细节。此外，它还结合了为EIEM量身定制的空间加权交叉熵损失函数，根据像素对边缘分割的重要性对不同区域引入约束，同时通过对正负样本的加权处理来缓解类别不平衡。
所提出的方法在2018年心房分割挑战赛数据集上得到验证。与其他先进算法相比，所提出的算法表现出显著改进，平均对称表面距离为0.684毫米，获得了值得称赞的Dice系数0.924，这表明增强边缘信息是有效的。
该方法为精确的LA定位和分割提供了一个实用框架，尤其增强了算法在改善不规则突出和离散多个目标的分割结果方面的有效性。此外，我们在医学分割十项全能挑战赛（MSD）的心脏数据集上评估了模型的通用性，证实了其在涉及LA分割的不同临床场景中的稳健性。
