School of Science, Northeastern University, Shenyang, China.
School of Data and Computer Science, Guangdong Peizheng College, Guangzhou, China.
Med Phys. 2023 Aug;50(8):4887-4898. doi: 10.1002/mp.16265. Epub 2023 Feb 14.
Pulmonary embolism is a kind of cardiovascular disease that threatens human life and health. Since pulmonary embolism exists in the pulmonary artery, improving the segmentation accuracy of pulmonary artery is the key to the diagnosis of pulmonary embolism. Traditional medical image segmentation methods have limited effectiveness in pulmonary artery segmentation. In recent years, deep learning methods have been gradually adopted to solve complex problems in the field of medical image segmentation.
Due to the irregular shape of the pulmonary artery and the adjacent-complex tissues, the accuracy of the existing pulmonary artery segmentation methods based on deep learning needs to be improved. Therefore, the purpose of this paper is to develop a segmentation network, which can obtain higher segmentation accuracy and further improve the diagnosis effect.
In this study, the pulmonary artery segmentation performance from the network model and loss function is improved, proposing a pulmonary artery segmentation network (PA-Net) to segment the pulmonary artery region from 2D CT images. Reverse Attention and edge attention are used to enhance the expression ability of the boundary. In addition, to better use feature information, the channel attention module is introduced in the decoder to highlight the important channel features and suppress the unimportant channels. Due to blurred boundaries, pixels near the boundaries of the pulmonary artery may be difficult to segment. Therefore, a new contour loss function based on the active contour model is proposed in this study to segment the target region by assigning dynamic weights to false positive and false negative regions and accurately predict the boundary structure.
The experimental results show that the segmentation accuracy of this proposed method is significantly improved in comparison with state-of-the-art segmentation methods, and the Dice coefficient is 0.938 ± 0.035, which is also confirmed from the 3D reconstruction results.
Our proposed method can accurately segment pulmonary artery structure. This new development will provide the possibility for further rapid diagnosis of pulmonary artery diseases such as pulmonary embolism. Code is available at https://github.com/Yuanyan19/PA-Net.
肺栓塞是一种危害人类生命健康的心血管疾病。由于肺栓塞存在于肺动脉中，提高肺动脉的分割准确性是肺栓塞诊断的关键。传统的医学图像分割方法在肺动脉分割中效果有限。近年来，深度学习方法已逐渐被应用于解决医学图像分割领域的复杂问题。
由于肺动脉的形状不规则，与相邻的复杂组织相邻，现有的基于深度学习的肺动脉分割方法的准确性需要提高。因此，本文的目的是开发一种分割网络，可以获得更高的分割精度，进一步提高诊断效果。
本研究从网络模型和损失函数两方面提高肺动脉分割性能，提出一种肺动脉分割网络（PA-Net），从 2D CT 图像中分割肺动脉区域。采用反向注意力和边缘注意力来增强边界的表达能力。此外，为了更好地利用特征信息，在解码器中引入通道注意力模块，突出重要的通道特征，抑制不重要的通道。由于边界模糊，靠近肺动脉边界的像素可能难以分割。因此，本研究提出了一种新的基于主动轮廓模型的轮廓损失函数，通过对假阳性和假阴性区域分配动态权重来分割目标区域，从而准确预测边界结构。
实验结果表明，与最先进的分割方法相比，该方法的分割精度有了显著提高，Dice 系数为 0.938±0.035，从 3D 重建结果也得到了验证。
我们提出的方法可以准确分割肺动脉结构。这一新的发展将为肺栓塞等肺动脉疾病的快速诊断提供可能。代码可在 https://github.com/Yuanyan19/PA-Net 上获得。
