To better retain the deep features of an image and solve the sparsity problem of the end-to-end segmentation model, we propose a new deep convolutional network model for medical image pixel segmentation, called MC-Net. The core of this network model consists of four parts, namely, an encoder network, a multiple max-pooling integration module, a cross multiscale deconvolution decoder network and a pixel-level classification layer. Each max-pooling layer (the pooling size of each layer is different) is spliced after each convolution to achieve the translation invariance of the feature maps of each submodule. The multiscale convolution of each submodule in the decoder network is cross-fused with the feature maps generated by the corresponding multiscale convolution in the encoder network. Using the above feature map processing methods solves the sparsity problem after the max-pooling layer-generating matrix and enhances the robustness of the classification. We compare our proposed model with the well-known Fully Convolutional Networks for Semantic Segmentation (FCNs), DecovNet, PSPNet, U-net, SgeNet and other state-of-the-art segmentation networks such as HyperDenseNet, MS-Dual, Espnetv2, Denseaspp using one binary dataset and two multiclass dataset and obtain encouraging experimental results.

为了更好地保留图像的深层特征并解决端到端分割模型的稀疏性问题，我们提出了一种新的用于医学图像像素分割的深度卷积网络模型，称为 MC-Net。该网络模型的核心由四部分组成，即编码器网络、多最大池化集成模块、跨多尺度反卷积解码器网络和像素级分类层。每个max-pooling层（每层的pooling大小不同）在每次卷积后拼接，实现每个子模块的特征图的平移不变性。解码器网络中每个子模块的多尺度卷积与编码器网络中相应多尺度卷积生成的特征图交叉融合。采用上述特征图处理方法解决了max-pooling层生成矩阵后的稀疏性问题，增强了分类的鲁棒性。我们将我们提出的模型与著名的语义分割全卷积网络 (FCN)、DekovNet、PSPNet、U-net、SgeNet 和其他最先进的分割网络（如 HyperDenseNet、MS-Dual、Espnetv2、 Denseaspp 使用一个二进制数据集和两个多类数据集，并获得了令人鼓舞的实验结果。