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Seg-CapNet: A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging
Journal of Computer Science and Technology ( IF 1.9 ) Pub Date : 2021-03-31 , DOI: 10.1007/s11390-021-0782-5
Yang-Jie Cao 1 , Shuang Wu 1 , Chang Liu 1 , Nan Lin 1 , Yuan Wang 2 , Cong Yang 1 , Jie Li 1, 3
Affiliation  

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI). In this paper, we propose a capsule-based neural network, named Seg-CapNet, to model multiple regions simultaneously within a single training process. The Seg-CapNet model consists of the encoder and the decoder. The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers, capsule layers, and fully-connected layers. And the decoder transforms the feature vectors into segmentation masks by up-sampling. Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers, which are conducive to the backpropagation of the model. The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture, as well as semantic features including the position and size of the objects, which is beneficial for improving the segmentation accuracy. The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge. Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7% and the average Hausdorff distance is reduced by 22%. The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions.



中文翻译:

Seg-CapNet:一种基于胶囊的神经网络,用于从心脏磁共振成像中分割左心室

深度神经网络 (DNN) 在医学图像分割中得到了广泛的研究。然而,现有的 DNN 通常需要为每个要分割的对象训练形状模型,这可能会在分割心脏磁共振成像 (MRI) 时产生违反心脏解剖结构的结果。在本文中,我们提出了一种基于胶囊的神经网络,称为 Seg-CapNet,用于在单个训练过程中同时对多个区域进行建模。Seg-CapNet 模型由编码器和解码器组成。编码器将输入图像转换为特征向量,这些特征向量表示要由卷积层、胶囊层和全连接层分割的对象。解码器通过上采样将特征向量转换为分割掩码。编码器中每个下采样层的特征图与对应的上采样层相连,有利于模型的反向传播。Seg-CapNet的输出向量包含灰度、纹理等低级图像特征,以及物体位置、大小等语义特征,有利于提高分割精度。所提出的模型在 2017 年自动心脏诊断挑战赛 (ACDC 2017) 和 Sunnybrook 心脏磁共振成像 (MRI) 分割挑战赛的开放数据集上得到验证。实验结果表明,Seg-CapNet的平均Dice系数提高了4.7%,平均Hausdorff距离降低了22%。

更新日期:2021-04-14
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