Study On Segmentation Method Of Chronic Stroke Lesions

Cerebral vascular accident(CVA),commonly known as stroke,is a type of acute vascular diseases.There is a high probability that CVA can lead to brain tissue damage because of the sudden rupture of brain vessels or vascular obstruction.Key consequences of stroke include acute disease onset,high disability rate,and high mortality.Early diagnosis of stroke is particularly important for effective treatment,and lesion segmentation is a key step in the workflow of stroke diagnosis.As a result,lesion segmentation becomes a hot topic attracting lots of researchers.In the acute phase of stroke,disease inspections based on low-resolution CT and structural MRI(e.g.T2-weighted,FLAIR,diffusion-weighted,and perfusion-weighted MRI)are typically performed,while in the chronic phase,high-resolution T1-weighted(Tlw)MRI is more applicable to evaluating brain structure changes.Nonetheless,currently,there are only a few large-scale neuroimaging studies targeting the automatic labeling of chronic stroke lesions in Tlw images.Considering the importance of automatic chronic stroke lesion segmentation,much more research in this direction is in urgent need.Brain image segmentation techniques have gone through several stages.In the first stage,direct manual segmentation was utilized relying on well-trained experts.The purely manual process was very time-consuming and depend heavily on the subjective perceptions of the experts.In the second stage,various semi-automatic and fully-automatic segmentation methods have been developed,including both traditional segmentation algorithms and machine learning-based methods.Semi-automatic segmentation proceeds according to the interactions between experts and computational models.For example,the region growing model starts with a region selected by the experts and subsequently segments the whole lesion by the computer.Compared with manual segmentation,these approaches can largely reduce the segmentation time.However,these methods fail in the presence of complex structures,including the white matter and the gray matter.With the development of artificial intelligence,fully automatic medical image segmentation is becoming increasingly popular.Fully automatic methods do not require manual interventions.They are superior in both diagnosing efficiency and effectiveness.High reproducibility results can be obtained as well.To address the above-mentioned limitations,this thesis conducted three major projects:(1)A deep neural network architecture,denoted as Multi-Scale Deep Fusion Network(MSDF-Net),was proposed to segment stroke lesions in Tlw images with high accuracy.MSDF-Net contains an atrous spatial pyramid pooling module to extract image features at different scales.A capsule block is included to specifically deal with the issue of combining different-view images.The proposed method is an end-to-end deep encoder-decoder neural network.The cross-connections between the encoder and the decoder guide the correct generation of high-resolution feature maps.Experimental results on a publicly available data set,ATLAS,for chronic or sub-acute stroke lesion segmentation show that the model can obtain a higher segmentation score than existing segmentation networks.(2)A Cross-Level fusion and Context Inference Network(CLCI-Net)is developed to further improve the stroke lesion segmentation accuracy.Specifically,a Cross-Level feature Fusion(CLF)strategy was developed to make full use of the different scale features across different levels;Extending Atrous Spatial Pyramid Pooling with CLF,we have enriched multi-scale features to handle the different lesion sizes;In addition,convolutional long short-term memory is employed to infer context information and thus capture fine structures to address the intensity similarity issue.The proposed method was evaluated on the same ATLAS data set,and the average value of dice is 58.10%,which is an increase of 4.10%compared to the most commonly used U-Net model.(3)An intelligent system for the analysis of chronic stroke lesions is built upon Python and PyQT5.With the system,MR image data can be loaded,three anatomical planes(sagittal plane,coronal plane,and transverse plane)of the image can be displayed,and the location of stroke lesions can be predicted.End-users can use it to load stroke data.Subsequently,the system will automatically predict the locations of chronic stroke lesions,and the predicted lesion regions will be shown on the screen for inspection.Besides,an image zooming function is available to zoom in or out the image for a better view.In addition the three views of the same image are registered.End-users can easily locate the same voxels on the other two views by pointing at the voxels in one view,which can help monitor the same image region from different perspectives.The system realizes,rapid computer-aided image visualization and analysis that is of great value to the clinical diagnosis and treatment of stroke.