A medical image segmentation method based on multi-dimensional statistical features

Medical image segmentation has important auxiliary significance for clinical diagnosis and treatment. Most of existing medical image segmentation solutions adopt convolutional neural networks (CNNs). Althought these existing solutions can achieve good image segmentation performance, CNNs focus on local information and ignore global image information. Since Transformer can encode the whole image, it has good global modeling ability and is effective for the extraction of global information. Therefore, this paper proposes a hybrid feature extraction network, into which CNNs and Transformer are integrated to utilize their advantages in feature extraction. To enhance low-dimensional texture features, this paper also proposes a multi-dimensional statistical feature extraction module to fully fuse the features extracted by CNNs and Transformer and enhance the segmentation performance of medical images. The experimental results confirm that the proposed method achieves better results in brain tumor segmentation and ventricle segmentation than state-of-the-art solutions.

Automated summary

In the field of medical image segmentation, traditional solutions mainly adopt convolutional neural networks (CNNs). This paper proposes a hybrid feature extraction network that combines CNNs and Transformer to better utilize global information for feature extraction and improve the segmentation performance of medical images. Additionally, a multi-dimensional statistical feature extraction module is also introduced to enhance low-dimensional texture features and further improve the segmentation results.