Research On Medical Image Segmentation Based On Deep Learning And Tensor Decomposition

In recent years,there has been a global rise in intelligent healthcare,which requires the use of big data,artificial intelligence,and high-performance computing as technical foundations.Clinical trial data is applied for interdisciplinary deep cross-fusion development based on clinical demands.Medical image segmentation is the basis for intelligent healthcare,playing a crucial clinical role in lesion area localization and recognition,as well as developing surgical plans.However,manual annotation of medical image target areas is a time-consuming and subjective task in clinical practice,posing significant challenges to clinical doctors performing manual segmentation.Therefore,it is of great significance to automatically obtain accurate and robust medical image segmentation results.However,medical images are different from natural images.They have small data volumes,high structural similarity within the same field,and diversity in detail.The target area in segmentation tasks is variable,and the processing is more complex compared to natural images,easily influenced by lesions,and accompanied by artifacts,noise,low contrast,edge blurring,and other characteristics.Additionally,medical image segmentation involves uncontrollable factors outside the image,such as equipment accuracy and patient cooperation.With the development of deep learning technology,automatic segmentation algorithms based on convolutional neural networks have shown excellent performance in the field of medical image segmentation.However,they lack expression of global information about the target.In addition,imbalanced tumor and organ categories and differences in spatial and temporal resolution on multi-modal images increase the complexity and variability of the target objects.Furthermore,most deep learning models suffer from problems such as excessive parameterization and high complexity.All of these pose challenges to automatic and accurate segmentation of medical images.To address these issues,this paper proposes a precise,robust,and efficient lightweight segmentation algorithm by combining the advantages of tensor decomposition and deep learning.The main contributions and innovations of this paper are focused on four aspects.1)A new encoder-decoder framework that cross-convolution Transformer(C2former)algorithm is put forward to solve the problem that the convolutional neural network receptive field is limited and difficult to capture the global information.The framework is designed specifically for medical image segmentation tasks.It uses an attention mechanism with convolutional characteristics to capture local features in both spatial and channel dimensions,and a self-attention mechanism to capture global features that capture long-range dependencies between different pixels.The model integrates the dependencies at both short and long ranges using window-based self-attention,enhancing the model’s ability to understand local-global features of the image.The experiments conducted on three publicly available medical image datasets show that the proposed method significantly improves the accuracy of medical image segmentation.2)A 3D medical image segmentation model that addresses the limitations of 2D segmentation methods is proposed in processing 3D images.The proposed model combines convolutional neural networks with a cascade Transformer structure.It uses the standard Transformer structure’s multi-head attention mechanism but increases the relationship between heads by cross-sharing to obtain more feature information and improve the model’s generalization ability.In addition,the model introduces multi-layer fusion modules to perform feature fusion after each layer to adapt to the segmentation target’s diverse characteristics.To reduce the attention mechanism’s parameter volume,the model uses a Tucker decomposition to construct a new attention mechanism expression.The model is evaluated on four different medical image segmentation datasets,including publicly available datasets and datasets from clinical settings,covering multiple imaging modes.The results show that the proposed method achieves good segmentation results in heart organ segmentation,brain tumor segmentation,abdominal organ segmentation,and uterus and tumor segmentation.Moreover,the compression module can ensure comparable segmentation performance while compressing parameters.3)This paper proposes a parallel algorithm that combines convolutional neural networks and Transformers to address the problem of detail loss in 3D medical image segmentation models based on the cascaded CNN-Transformer structure.The algorithm uses shared convolution to reduce unnecessary parameter numbers and enables interaction between local and global features.To address the limitations of traditional channel attention mechanisms that only use global average pooling and the lowest frequency basis product,this paper proposes a new global attention mechanism that uses 3D-DCT instead of global average pooling as a skip-connection global feature extraction module for extracting global features.To solve the problem of additional parameters brought by the parallel structure,tensor ring decomposition is used to compress the CNN and Transformer structures separately.In the compression process,the compression parameters are obtained by automatically calculating the compression hyperparameters.The results show that the proposed compression module can achieve comparable or better performance while reducing the parameter amount.The algorithm is evaluated on four datasets,including three benchmark datasets and one clinical dataset.The results show that the proposed algorithm achieves good segmentation results on different datasets.Quantitative analysis indicates that the proposed method’s statistical analysis results are consistent with clinical experts’ results.Therefore,this algorithm has broad application prospects and can provide an effective solution for medical image segmentation tasks.4)This paper analyzes and studies the clinical application requirements of multi-modal medical image segmentation systems,and implements the proposed lightweight parallel method to develop a clinical application system for multi-modal medical image segmentation.The entire segmentation system is divided into several modules,including the preprocessing module,training module,segmentation module,and post-processing module.The preprocessing module takes the original medical data as input and outputs multi-modal image formats that can be recognized by the segmentation module.The segmentation module takes the preprocessed medical image data as input and outputs corresponding segmented results of organs or lesions.The post-processing module optimizes the segmentation results by taking the segmented organ or tumor images as input.In addition,for specific diseases or medical tasks in practical clinical applications,it may be necessary to use customized medical image datasets to train specific segmentation models.This system provides an interactive user interface that allows users to easily upload and manage their own datasets,specify segmentation tasks for the data,and upload and use their own medical image datasets to train specific segmentation models.