With the development of deep learning, medical image segmentation in computer-aided diagnosis has become a research hotspot. Recently, UNet and its variants have become the most powerful medical image segmentation methods. However, these methods suffer from (1) insufficient sensing field and insufficient depth; (2) computational nonlinearity and redundancy of channel features; and (3) ignoring the interrelationships among feature channels. These problems lead to poor network segmentation performance and weak generalization ability. Therefore, first of all, we propose an effective replacement scheme of UNet base block, Double residual depthwise atrous convolution (DRDAC) block, to effectively improve the deficiency of receptive field and depth. Secondly, a new linear module, the Multi-scale frequency domain filter (MFDF), is designed to capture global information from the frequency domain. The high order multi-scale relationship is extracted by combining the depthwise atrous separable convolution with the frequency domain filter. Finally, a channel attention called Axial selection channel attention (ASCA) is redesigned to enhance the network’s ability to model feature channel interrelationships. Further, we design a novel frequency domain medical image segmentation baseline method FDFUNet based on the above modules. We conduct extensive experiments on five publicly available medical image datasets and demonstrate that the present method has stronger segmentation performance as well as generalization ability compared to other state-of-the-art baseline methods.

中文翻译：

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基于多尺度频域滤波器的医学图像分割网络

随着深度学习的发展，计算机辅助诊断中的医学图像分割已成为研究热点。最近，UNet 及其变体已成为最强大的医学图像分割方法。然而，这些方法都存在以下问题：（1）传感视野和深度不够； (2)通道特征的计算非线性和冗余性； (3)忽略特征通道之间的相互关系。这些问题导致网络分割性能差、泛化能力弱。因此，首先，我们提出了一种UNet基础块的有效替代方案——双残差深度有孔卷积（DRDAC）块，以有效改善感受野和深度的不足。其次，设计了一个新的线性模块，即多尺度频域滤波器（MFDF），以从频域捕获全局信息。通过将深度有孔可分离卷积与频域滤波器相结合来提取高阶多尺度关系。最后，重新设计了称为轴选择通道注意（ASCA）的通道注意，以增强网络对特征通道相互关系进行建模的能力。进一步，我们基于上述模块设计了一种新颖的频域医学图像分割基线方法FDFUNet。我们对五个公开的医学图像数据集进行了广泛的实验，并证明与其他最先进的基线方法相比，本方法具有更强的分割性能和泛化能力。