Purpose

The purpose of this work is to segment multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) images, in which lesions in different sizes are segmented with appropriate accuracy. Automated segmentation as a powerful tool can assist professionals to increase the accuracy of disease diagnosis and its level of progression.

Methods

We present a deep neural network based on the U-Net architecture in which wavelet transform-based pooling replaces max pooling. In the first part of the network, the wavelet transform is used, and in the second part, it’s inverse. In addition to decomposing the input image and reducing its size, the wavelet transform highlights sharp changes in the image and better describes local features. This transform has the multi-resolution characteristic, so its use provides improvement in the detection of lesions of different sizes and segmentation.

Results

The results of this study show that the proposed method has a better Dice similarity coefficient (DSC) value compared to the max pooling and average pooling methods.

Conclusion

The proposed method has better results for segmenting MS lesions of different sizes in MRI images than the max and average pooling methods and other methods studied.

中文翻译：

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使用基于小波池化的 U-Net 从脑 MRI 中分割多发性硬化病灶

目的

这项工作的目的是在磁共振成像( MRI) 图像中分割多发性硬化 (MS) 病变，其中以适当的精度分割不同大小的病变。自动分割作为一种强大的工具可以帮助专业人员提高疾病诊断的准确性及其进展水平。

方法

我们提出了一个基于 U-Net 架构的深度神经网络，其中基于小波变换的池化代替了最大池化。在网络的第一部分，使用小波变换，在第二部分，它是逆的。除了分解输入图像并减小其尺寸外，小波变换还突出了图像的急剧变化并更好地描述了局部特征。这种变换具有多分辨率特性，因此它的使用为不同大小病变的检测和分割提供了改进。

结果

本研究的结果表明，与最大池化和平均池化方法相比，所提出的方法具有更好的 Dice 相似系数（DSC）值。

结论

与最大和平均池化方法以及其他研究的方法相比，所提出的方法在分割 MRI 图像中不同大小的 MS 病变方面具有更好的结果。