Department of Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan.
Curr Med Imaging. 2020;16(3):206-213. doi: 10.2174/1573405615666191227123733.
Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall.
To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach.
Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts.
We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method.
Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use.
疤痕组织的图像评估在心血管疾病的诊断中起着重要作用。 疤痕组织的分割是评估疤痕组织形态的第一步。 然后，使用 CT 图像，可以应用深度学习方法来识别左心室心内膜壁上可能存在的疤痕组织。
开发一种使用深度学习方法自动检测左心室心内膜疤痕组织的方法。
从序列中的每个图像中提取心内膜壁的像素值。 进行形态学操作，包括定义疤痕组织可能占主导地位的左心室心内膜壁区域。 卷积神经网络（CNN）是一种深度学习应用程序，它允许通过应用基于像素值的概念，从延迟增强心脏 CT 图像中选择适当的特征，以区分心内膜疤痕和左心室的健康组织。
使用基于 CNN 的方法，我们在检测心内膜疤痕方面实现了 89.23%的准确率、91.11%的敏感性和 87.75%的特异性。
我们的研究结果表明，基于 CNN 的方法在心内膜疤痕检测中具有较高的准确性，这是目前最广泛使用的基于像素的深度学习方法。 本研究为评估成像方式中的疤痕组织提供了新的方向，并为这些算法的临床应用提供了潜在途径。 此外，与文献中的方法相比，该方法在向临床应用转化方面具有特定优势。
