Ba Mahel Abduljabbar S, Al-Gaashani Mehdhar S A M, Alotaibi Fahad Mushabbab G, Alkanhel Reem Ibrahim
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 610056, China.
Bioengineering (Basel). 2025 Jun 17;12(6):665. doi: 10.3390/bioengineering12060665.
The highly precise and trustworthy segmentation of the left ventricle (LV) and myocardium is critical for diagnosing and treating cardiovascular disorders, which includes persistent microvascular obstruction (MVO) as well as myocardial infarction (MI) diseases. This process improves diagnostic accuracy and optimizes the planning and implementation of therapeutic interventions, ultimately improving the quality of care and patient prognosis. Limitations of earlier investigations include neglecting the complex image pre-processing required to accurately delineate areas of the LV and myocardium (Myo) in MRI and the absence of a substantial, high-quality dataset. Thus, this paper presents a comprehensive end-to-end framework, which includes contrast-limited adaptive histogram equalization (CLAHE) and bilateral filtering methods for image pre-processing and the development and implementation of a proposed deep model for left ventricular and myocardium segmentation. This study utilizes the EMIDEC database for the training and assessment of the model, allowing for a detailed comparative analysis with six state-of-the-art (SOTA) segmentation models. This approach provides a high accuracy and reliability for the segmentation that is crucial for the diagnosis and treatment of cardiovascular disorders. The achievements of the proposed model are demonstrated by high average values of segmentation rates, such as an Intersection over Union (IoU) of 93.73%, Recall of 96.54%, Dice coefficient of 96.70%, Precision of 96.86%, and F1-score of 96.70%. To verify the generalization capability, we assessed our suggested model on five supplementary databases, which substantiates its exceptional efficiency and adaptability in a diverse environment. The presented findings demonstrate that the proposed deep model surpasses current methods, offering more a precise and resilient segmentation of cardiac structures.
左心室（LV）和心肌的高精度、可靠分割对于心血管疾病的诊断和治疗至关重要，这些疾病包括持续性微血管阻塞（MVO）以及心肌梗死（MI）等。这一过程提高了诊断准确性，优化了治疗干预措施的规划与实施，最终改善了护理质量和患者预后。早期研究的局限性包括忽视了在MRI中准确描绘LV和心肌区域所需的复杂图像预处理，以及缺乏大量高质量数据集。因此，本文提出了一个全面的端到端框架，其中包括用于图像预处理的对比度受限自适应直方图均衡化（CLAHE）和双边滤波方法，以及用于左心室和心肌分割的深度模型的开发与实施。本研究利用EMIDEC数据库对模型进行训练和评估，从而能够与六种先进的（SOTA）分割模型进行详细的对比分析。这种方法为分割提供了高精度和可靠性，这对于心血管疾病的诊断和治疗至关重要。所提出模型的成果通过高分割率平均值得到证明，如交并比（IoU）为93.73%、召回率为96.54%、Dice系数为96.70%、精确率为96.86%以及F1分数为96.70%。为了验证泛化能力，我们在五个补充数据库上评估了我们建议的模型，这证实了其在多样化环境中的卓越效率和适应性。所呈现的研究结果表明，所提出的深度模型超越了现有方法，能够对心脏结构进行更精确、更可靠的分割。
