目的 针对现有神经网络模型需要对左心室心肌内膜和外膜单独建模的问题，本文提出了一种基于胶囊结构的心脏磁共振图像（magnetic resonance imaging，MRI）分割模型Seg-CapNet，旨在同时提取心肌内膜和外膜，并保证两者的空间位置关系。方法 首先利用胶囊网络将待分割目标转换成包含目标相对位置、颜色以及大小等信息的向量，然后使用全连接将这些向量的空间关系进行重组，最后采用反卷积对特征图进行上采样，将分割图还原为输入图像尺寸。在上采样过程中将每层特征图与卷积层的特征图进行连接，有助于图像细节还原以及模型的反向传播，加快训练过程。Seg-CapNet的输出向量不仅有图像的灰度、纹理等底层图像特征，还包含目标的位置、大小等语义特征，有效提升了目标图像的分割精度。为了进一步提高分割质量，还提出了一种新的损失函数用于约束分割结果以保持多目标区域间的相对位置关系。结果 在ACDC（automated cardiac diagnosis challenge）2017、MICCAI（medical image computing and computer-assisted intervention）2013和MICCAI2009等3个心脏MRI分割竞赛的公开数据集上对Seg-CapNet模型进行训练和验证，并与神经网络分割模型U-net和SegNet进行对比。实验结果表明，相对于U-Net和SegNet，Seg-CapNet同时分割目标重叠区域的平均Dice系数提升了3.5%，平均豪斯多夫距离（Hausdorff distance，HD）降低了18%。并且Seg-CapNet的参数量仅为U-Net的54%、SegNet的40%，在提升分割精度的同时，降低了训练时间和复杂度。结论 本文提出的Seg-CapNet模型在保证同时分割重叠区域目标的同时，降低了参数量，提升了训练速度，并保持了较好的左心室心肌内膜和外膜分割精度。

Objective Image segmentation tasks suffer from the problem in which multiple overlapping regions are required to be extracted, such as the division of the endocardium and epicardium of the heart’s left ventricle. Existing neural network segmentation models typically segment the target based on pixel classification due to the overlapping of pixels in the two regions and then convert the segmentation problem into a classification problem. However, the overlapping area of pixels may not be simultaneously classified well. In general, existing neural networks must train model parameters for each target to obtain accurate segmentation results, reducing segmentation efficiency. To address these issues, we propose a segmentation model, called Seg-CapNet, which is based on a capsule network structure. Method Current segmentation models based on convolutional neural networks control the size of feature maps through operations, such as maximum or average pool, and transmit image feature information from the upper layer to the next layer. Such pooling operations lose the spatial information of components in the process of information transmission. Therefore, the proposed Seg-CapNet model uses a capsule network structure to extract vectors that contain spatial, color, size, and other target information. Compared with current network structures, the output of a capsule network is in vector form, and the information of the target is included in the entity vector through routing iteration. Seg-CapNet utilizes this feature to strip overlapping objects from the image space and convert them into noninterference feature vectors, separating objects with overlapping regions. Then, the spatial position relation of multiple target vectors are reconstructed using fully connected layers. Lastly, the reconstructed image is up-sampled and the segmented image is restored to the same size as the input image. During up-sampling, the feature graph of the up-sampling layer and that of the convolutional layer are skip-connected. This process is conducive to restoring image details and accelerating the training process while the model is backpropagating. To improve segmentation results, we also design a new loss function for constraining segmentation results to ensure that they can maintain a relative position relationship among multiple target areas to follow cardiac morphology. In the loss function based on the Dice coefficient, the ratio constraint of the area beyond the epicardium boundary to the area of the endocardium is added, and thus, the area of the endocardium is divided as far as possible within the outer membrane. To prevent the ratio from becoming too small to influence parameter updating in the backpropagation process, we control its value within an appropriate range through exponential transformation and keep it synchronized with the loss function based on the Dice coefficient. This method is implemented using Python 3.6 and TensorFlow on Nvidia Tesla K80 GPU, Intel E5-2650 CPU, and 10 G main memory. The learning rate is 0.001. Image sizes from different devices are inconsistent because data sources are collected from different imaging devices. However in cardiac magnetic resonance imaging (MRI), the heart is typically located near the center. Therefore, the 128×128 pixel region centered on an image is extracted as the size of the input model image, and image size can be unified, including the image of the whole heart. Result We train and verify the Seg-CapNet model on the automated cardiac diagnosis challenge(ACDC)2017, medical image computing and computer-assisted intervention(MICCAI)2013, and MICCAI2009 datasets, and then compare the results with those of the neural network segmentation models, U-Net and SegNet. Experimental results show that the average Dice coefficient of our model increased by 4.7% and the average Hausdorff distance decreased by 22% compared with those of U-Net and SegNet. Moreover, the number of Seg-CapNet parameters was only 54% of that of U-Net and 40% of that of SegNet. Our results illustrate that the proposed model improves segmentation accuracy and reduces training time and complexity. In addition, we validate the performance of the proposed loss function on the ACDC2017 dataset. By comparing the segmentation results of the model before and after random selection and adding the constraint loss function, the new loss function avoids the internal region located outside the epicardium, violating the anatomical structure of the heart. Simultaneously, we calculate the mean Dice value of the segmentation results before and after adding the constraint to the loss function. The experimental results show that Dice value of the segmentation results of the left ventricular endocardium and epicardium with the new loss function increases by an average of 0.6%. Conclusion We propose a Seg-CapNet model that ensures the simultaneous segmentation of multiple overlapping targets, reduces the number of participants, and accelerates the training process. The results show that our model can maintain good segmentation accuracy while segmenting two overlapping regions of the heart’s left ventricle in MRI.