Search

Image segmentation in brain magnetic resonance imaging (MRI) largely relates to dividing brain tissue into components like white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Using the segmentation outputs, medical images can be 3D reconstructed and visualized efficiently. It is common for MRI pictures to have issues such as partial volume effects, asymmetrical grayscale, and noise. As a result, high accuracy in brain MRI picture segmentation is challenging to achieve in practical applications. In this paper, we developed an effective algorithm for brain MRI image segmentation utilizing a combination of statistical and partial differential equation-based approaches, based on a neuro-mechanical model. The findings of this work demonstrate that by combining various segmentation approaches, it is possible to quickly segment brain MRI data at a degree of precision necessary for different applications. Here, we show that when we use nonlinear filtering, k -means clustering, and active contour modeling, we can get very good results when we segment brain MRI images. It is clear that the proposed approach has higher segmentation performance and can properly separate brain tissue from a large number of MRI images. [ABSTRACT FROM AUTHOR]

In the current medical image segmentation network, the combination of CNN and Transformer has become a mainstream trend. However, the inherent limitations of convolution operation in CNN and insufficient information interaction in Transformer affect the segmentation performance of the network. To solve these problems, an integrated self-attention and convolution medical image segmentation network (ISC-TransUNet) is proposed in this paper. The network consists of encoder, decoder and jump connection. First, the encoder uses a hybrid structure of BoTNet and Transformer to capture more comprehensive image information and reduce additional computing overhead. Then, the decoder uses an upper sampler cascaded by multiple DUpsampling upper blocks to accurately recover the pixel-level prediction. Finally, the feature fusion of encoder and decoder at different resolutions is realized by ResPath jump connection, which reduces the semantic difference between encoder and decoder. Through experiments on the Synapse multi-organ segmentation dataset, compared with the baseline model TransUNet, Dice similarity coefficient of ISC-TransUNet was improved by 1.13%, Hausdorff distance was reduced by 2.38%, and weight was maintained. The experimental results show that the network can effectively segment tissues and organs in medical images, which has important theoretical significance and application value for intelligent clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]