College of Mechanical Engineering, Donghua University, Shanghai 201620, China.
Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China.
Sensors (Basel). 2023 Jan 7;23(2):690. doi: 10.3390/s23020690.
Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network's ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset.
使用磁共振图像准确分割左心房结构，为心房颤动（AF）的诊断及其机器人手术治疗提供了重要依据。本研究提出了一种基于序列关系学习和多尺度特征融合的图像分割方法，用于心脏磁共振图像的 3D 到 2D 序列转换，以及不同切片中左心房结构的不同尺度。首先，设计了一个具有注意力模块的卷积神经网络层，以提取和融合图像中不同尺度的上下文信息，通过图像中不同区域特征之间的相关性来增强目标特征，并提高网络区分左心房结构的能力。其次，设计了一个面向二维图像的循环神经网络层，通过模拟连续切片之间的连续关系，捕捉相邻切片中左心房结构的相关性。最后，构建了一个组合损失函数，以减少正负样本不平衡的影响，提高模型稳定性。基于 LASC2013（2013 年左心房分割挑战赛）数据集，Dice、IoU 和 Hausdorff 距离值分别达到 90.73%、89.37%和 4.803mm；基于 ASC2018（2018 年心房分割挑战赛）数据集，Dice、IoU 和 Hausdorff 距离值分别达到 92.05%、89.41%和 9.056mm。
