Research On Tumor Segmentation And Quality Enhancement Of Brain Images Based On Intelligent Reasoning

Medical image analysis refers to the processing,interpretation,and extraction of information related to the patient’s anatomical structures and functions from medical images.Traditional methods for image diagnosis rely heavily on the experience of radiologists,which are subjective,less reproducible,and lack quantitative capabilities.There is a pressing need for intelligent and automated image analysis techniques to assist radiologists in improving the speed and accuracy of diagnosis.Deep learning models,trained on large amounts of medical image data,learn feature representations and pattern recognition,enabling automated and accurate tasks such as lesion localization,segmentation,classification,quantification,and prediction.Deep learning models have achieved levels of accuracy comparable to human experts in various diagnostic tasks.However,medical image analysis poses significant challenges due to limited data,difficulties in annotation,class imbalance,and lack of interpretability,making it a challenging task.In this paper,we conducted comprehensive and in-depth research to address these issues and aimed to improve the capability of deep learning methods in representing lesion features,enhancing the accuracy of lesion segmentation,and improving the quality of medical images.The research content of this paper can be summarized into the following four parts.1)A cross-modal MRI images segmentation algorithm for brain tumors based on contrastive learning is proposed.Traditional deep learning-based tumor segmentation methods assign each pixel to a specific semantic class based on contextual information from image slices.However,these methods often overlook the correlation between pixels with the same semantic information across different image slices.Contrastive learning can capture the correlation between images with the same semantic information in the entire dataset.Therefore,this paper proposes a strategy to apply contrastive learning to modality data for coarse tumor segmentation and combines it with semi-supervised learning for fine segmentation of multi-modal images.The algorithm leverages contrastive learning to maximize the feature differences between tumors and normal tissues using FLAIR modality images that provide complete brain tissue boundaries.The coarse segmentation network is used to achieve image-level tumor segmentation.The generated tumor region mask from coarse segmentation is then used to reduce the influence of normal brain tissue on tumor segmentation and combined with semi-supervised learning strategy for fine segmentation.Experimental results demonstrate that the proposed algorithm surpasses the current state-ofthe-art methods and proves the effectiveness and potential of contrastive learning and semisupervised learning strategies in medical image segmentation.2)A deep mutual learning algorithm for brain tumor MRI images segmentation based on fusion network is proposed.Traditional brain tumor image segmentation algorithms have achieved good results,but there is an extreme data imbalance issue in different tumor regions,leading to biases and inaccuracies in accurate segmentation using traditional methods.To address this problem,the paper introduces the deep mutual learning strategy and integrates transformers into the encoder and decoder of the U-Net structure.In the network,predictions from up sampling layers are used for deep supervision training to enlarge the receptive field and extract features.The shallowest feature layer is used to supervise the subsequent feature layer mapping to retain more edge information to guide accurate subregion segmentation.The deepest classification logic map supervises the previous layer’s logic map to obtain more semantic information for differentiating sub-regions of tumors.Furthermore,feature maps and classification logic mutually supervise each other to improve overall segmentation accuracy.Experimental results on benchmark datasets show significant performance gains compared to existing classical methods.3)A super-resolution reconstruction algorithm of brain MRI images based on second-order propagation and grid connection is proposed.A brain MRI image super-resolution reconstruction algorithm based on second-order propagation and grid connections has been proposed.Traditional super-resolution algorithms for MRI images map low-resolution slice images to high-resolution ones by learning prior knowledge from image slices.MRI images are typically three-dimensional data,where there is often correlation between adjacent slices in terms of structure and organization,including anatomical continuity and spatial similarity.However,traditional MRI super-resolution algorithms often fail to effectively utilize the correlation between slices.Therefore,this paper proposes a brain MRI super-resolution algorithm based on second-order propagation and grid connections to fully exploit the correlation between slice images.Specifically,the brain MRI volume data is decomposed into a two-dimensional slice sequence.Spatial features of each slice are extracted using convolutional networks,and a second-order Markov propagation strategy is employed to fuse the spatial features of the current slice with the relevant features from preceding slices.This enables efficient propagation of the fused features in the slice sequence,thereby enhancing the representational capacity of the features.Subsequently,grid connections are used to combine second-order Markov chains in different directions,allowing multiple bidirectional transfers of correlated features between slices.This refines the spatial features with respect to different slices in the sequence,further enhancing their ability to describe details.Finally,a reconstruction module is employed to generate high-resolution slice images.Experimental results on the IXI dataset demonstrate that this method significantly improves the reconstruction quality of medical MRI images and outperforms the state-ofthe-art algorithms.4)A super-resolution reconstruction algorithm of brain MRI images based on frequencyTransformer is proposed.The spatial-domain Transformer-based image super-resolution reconstruction utilizes self-attention mechanisms for global contextual modeling to capture long-range dependencies and improve the accuracy of super-resolution.However,the selfattention mechanism acts similar to a low-pass filter,ignoring high-frequency information and causing inadequate restoration of medical image details.Moreover,MRI images themselves undergo a frequency inverse transform to convert frequency components from the k-space to spatial positions in the image.Therefore,this paper introduces a frequencyTransformer transformation that directly extracts frequency-domain features from MRI signals and combines them with deep features of medical body data for high-resolution image reconstruction.First,the block of MRI image slices obtained after segmentation is transformed into a spectrogram.The Transformer transformation is applied equally to each frequency band to effectively preserve high-frequency detail information.Then,deformable convolutions are used for registration and alignment of different depth images,and the aligned images are transformed using the Transformer transformation to effectively preserve deep visual information.Finally,the frequency features and depth information are combined for high-resolution image reconstruction.Experimental results on the Bra TS18 and IXI datasets show significant improvements in reconstructed details on different low-quality MRI slices compared to existing SOTA methods.This dissertation explores key techniques in medical image analysis,including feature representation,super-resolution reconstruction,and lesion region segmentation.These techniques effectively enhance the prediction accuracy,scalability,and learning efficiency of medical image analysis.The findings of this study provide important technical support for the promotion and application of medical image segmentation,as well as open up new avenues for research in medical image enhancement.Therefore,this research holds significant theoretical significance and practical value.