Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.
Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.
Technol Health Care. 2024;32(6):4267-4289. doi: 10.3233/THC-240062.
The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals realize that manually segmenting data to evaluate these processes takes a lot of time, effort when diagnosing heart diseases. Yet, manually segmenting these images is labour-intensive and may reduce diagnostic accuracy.
OBJECTIVE/METHODS: This paper, propose a combination of different deep neural networks for semantic segmentation of the left ventricle based on Tri-Convolutional Networks (Tri-ConvNets) to obtain highly accurate segmentation. CMRI images are initially pre-processed to remove noise artefacts and enhance image quality, then ROI-based extraction is done in three stages to accurately identify the LV. The extracted features are given as input to three different deep learning structures for segmenting the LV in an efficient way. The contour edges are processed in the standard ConvNet, the contour points are processed using Fully ConvNet and finally the noise free images are converted into patches to perform pixel-wise operations in ConvNets.
RESULTS/CONCLUSIONS: The proposed Tri-ConvNets model achieves the Jaccard indices of 0.9491 ± 0.0188 for the sunny brook dataset and 0.9497 ± 0.0237 for the York dataset, and the dice index of 0.9419 ± 0.0178 for the ACDC dataset and 0.9414 ± 0.0247 for LVSC dataset respectively. The experimental results also reveal that the proposed Tri-ConvNets model is faster and requires minimal resources compared to state-of-the-art models.
左心室分割（LVS）对于评估心脏功能至关重要。在全球范围内，心血管疾病是大多数死亡的主要原因，对健康构成重大威胁。近年来，由于 LVS 能够测量心肌质量、舒张末期容积和射血分数等重要参数，因此受到了广泛关注。医疗专业人员意识到，手动分割数据来评估这些过程需要花费大量时间和精力来诊断心脏病。然而，手动分割这些图像既费时费力，又可能降低诊断准确性。
目的/方法：本文提出了一种基于三卷积网络（Tri-ConvNets）的左心室语义分割的深度神经网络组合，以获得高度准确的分割。首先对 CMRI 图像进行预处理，以去除噪声伪影并提高图像质量，然后分三个阶段进行基于 ROI 的提取，以准确识别 LV。将提取的特征作为输入提供给三个不同的深度学习结构，以高效地分割 LV。轮廓边缘在标准 ConvNet 中处理，轮廓点在全卷积网络中处理，最后将无噪声图像转换为补丁，以便在 ConvNets 中执行像素级操作。
结果/结论：所提出的 Tri-ConvNets 模型在 sunny brook 数据集上的 Jaccard 指数为 0.9491 ± 0.0188，在 York 数据集上的 Jaccard 指数为 0.9497 ± 0.0237，在 ACDC 数据集上的 dice 指数为 0.9419 ± 0.0178，在 LVSC 数据集上的 dice 指数为 0.9414 ± 0.0247。实验结果还表明，与最先进的模型相比，所提出的 Tri-ConvNets 模型更快，所需资源更少。
