PDF(Pubmed)
Abstract：
BACKGROUND: Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on image with balanced foreground and background, but it will loss the characteristics of small targets on image with imbalanced foreground and background after multiple convolutions.
METHODS: In order to retain the features of small targets in the deep network, we proposed a new medical image segmentation model based on the U-Net with squeeze-and-excitation and attention modules which form a spiral closed path,callled as Spiral Squeeze-and-Excitation and Attention NET (SEA-NET) in this paper. The segmentation model used squeeze-and-extraction modules to adjust the channel information to enhance the useful information and used attention modules to adjust the spatial information of the feature map to highlight the target area for small target segmentation when up-sampling. The deep semantic information is integrated into the shallow feature map by the attention model. Therefore, the deep semantic information cannot be scattered by continuous up-sampling. We used cross entropy loss + Tversky loss function for fast convergence and well processing the imbalanced data sets. Our proposed SEA-NET was tested on the brain MRI dataset LPBA40 and peripheral blood smear images.
CONCLUSIONS: On brain MRI data, the average value of the Dice coefficient we obtained reached 98.1[Formula: see text]. On the peripheral blood smear dataset, our proposed model has a good segmentation effect on adhesion cells.
RESULTS: The experimental results proved that the proposed SEA-Net performed better than U-Net, U-Net++, etc. in medical image segmentation.
METHODS: In order to retain the features of small targets in the deep network, we proposed a new medical image segmentation model based on the U-Net with squeeze-and-excitation and attention modules which form a spiral closed path,callled as Spiral Squeeze-and-Excitation and Attention NET (SEA-NET) in this paper. The segmentation model used squeeze-and-extraction modules to adjust the channel information to enhance the useful information and used attention modules to adjust the spatial information of the feature map to highlight the target area for small target segmentation when up-sampling. The deep semantic information is integrated into the shallow feature map by the attention model. Therefore, the deep semantic information cannot be scattered by continuous up-sampling. We used cross entropy loss + Tversky loss function for fast convergence and well processing the imbalanced data sets. Our proposed SEA-NET was tested on the brain MRI dataset LPBA40 and peripheral blood smear images.
CONCLUSIONS: On brain MRI data, the average value of the Dice coefficient we obtained reached 98.1[Formula: see text]. On the peripheral blood smear dataset, our proposed model has a good segmentation effect on adhesion cells.
RESULTS: The experimental results proved that the proposed SEA-Net performed better than U-Net, U-Net++, etc. in medical image segmentation.
摘要：
背景：医学图像分割是大多数医学图像分析中的重要处理步骤。因此，他们需要高精度和鲁棒性。当前基于深度神经网络的医学分割方法对前景和背景均衡的图像具有良好的分割效果,但是经过多次卷积后，会失去前景和背景不平衡的图像上小目标的特征。
方法：为了保留深度网络中小目标的特征，提出了一种新的基于U-Net的医学图像分割模型,本文称之为螺旋挤压激励和注意力网(SEA-NET)。分割模型使用挤压和提取模块调整通道信息以增强有用信息，并使用注意力模块调整特征图的空间信息以突出目标区域，以便在上采样时进行小目标分割。通过注意力模型将深层语义信息集成到浅层特征图中。因此,深度语义信息不能通过连续的上采样来分散。我们使用交叉熵损失+Tversky损失函数来快速收敛并很好地处理不平衡数据集。我们提出的SEA-NET在脑MRI数据集LPBA40和外周血涂片图像上进行了测试。
结论：关于脑MRI数据，我们得到的骰子系数的平均值达到98.1[公式：见正文]。在外周血涂片数据集上，我们提出的模型对粘连细胞有很好的分割效果。
结果：实验结果证明，所提出的SEA-Net的性能优于U-Net，U-Net++,等。在医学图像分割中。
方法：为了保留深度网络中小目标的特征，提出了一种新的基于U-Net的医学图像分割模型,本文称之为螺旋挤压激励和注意力网(SEA-NET)。分割模型使用挤压和提取模块调整通道信息以增强有用信息，并使用注意力模块调整特征图的空间信息以突出目标区域，以便在上采样时进行小目标分割。通过注意力模型将深层语义信息集成到浅层特征图中。因此,深度语义信息不能通过连续的上采样来分散。我们使用交叉熵损失+Tversky损失函数来快速收敛并很好地处理不平衡数据集。我们提出的SEA-NET在脑MRI数据集LPBA40和外周血涂片图像上进行了测试。
结论：关于脑MRI数据，我们得到的骰子系数的平均值达到98.1[公式：见正文]。在外周血涂片数据集上，我们提出的模型对粘连细胞有很好的分割效果。
结果：实验结果证明，所提出的SEA-Net的性能优于U-Net，U-Net++,等。在医学图像分割中。
