Automatic and accurate semantic segmentation of pathological structures in medical images is challenging because of noisy disturbance, deformable shapes of pathology, and low contrast between soft tissues. Classical superpixel-based classification algorithms suffer from edge leakage due to complexity and heterogeneity inherent in medical images. Therefore, we propose a deep U-Net with superpixel region merging processing incorporated for edge enhancement to facilitate and optimize segmentation. Our approach combines three innovations: (1) different from deep learning--based image segmentation, the segmentation evolved from superpixel region merging via U-Net training getting rich semantic information, in addition to gray similarity; (2) a bilateral filtering module was adopted at the beginning of the network to eliminate external noise and enhance soft tissue contrast at edges of pathogy; and (3) a normalization layer was inserted after the convolutional layer at each feature scale, to prevent overfitting and increase the sensitivity to model parameters. This model was validated on lung CT, brain MR, and coronary CT datasets, respectively. Different superpixel methods and cross validation show the effectiveness of this architecture. The hyperparameter settings were empirically explored to achieve a good trade-off between the performance and efficiency, where a four-layer network achieves the best result in precision, recall, F-measure, and running speed. It was demonstrated that our method outperformed state-of-the-art networks, including FCN-16s, SegNet, PSPNet, DeepLabv3, and traditional U-Net, both quantitatively and qualitatively. Source code for the complete method is available at https://github.com/Leahnawho/Superpixel-network.

中文翻译：

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基于深度网络的超像素区域合并用于医学图像分割

由于噪声干扰、病理形状可变形以及软组织之间的低对比度，对医学图像中的病理结构进行自动和准确的语义分割具有挑战性。由于医学图像固有的复杂性和异质性，经典的基于超像素的分类算法存在边缘泄漏。因此，我们提出了一种带有超像素区域合并处理的深度 U-Net，用于边缘增强，以促进和优化分割。我们的方法结合了三个创新：（1）与基于深度学习的图像分割不同，分割是从超像素区域合并演变而来，通过 U-Net 训练获得丰富的语义信息，以及灰度相似性；(2)网络开始采用双边滤波模块，消除外部噪声，增强病理边缘软组织对比度；(3) 在每个特征尺度的卷积层之后插入归一化层，防止过拟合，增加对模型参数的敏感性。该模型分别在肺 CT、脑 MR 和冠状动脉 CT 数据集上得到验证。不同的超像素方法和交叉验证显示了这种架构的有效性。超参数设置经过经验探索，以实现性能和效率之间的良好平衡，其中四层网络在精度、召回率、F-measure 和运行速度方面取得了最佳结果。证明我们的方法优于最先进的网络，包括 FCN-16s、SegNet、PSPNet、DeepLabv3、和传统的 U-Net，无论是定量还是定性。完整方法的源代码可在 https://github.com/Leahnawho/Superpixel-network 获得。