Left Ventricle?Right Ventricle And Left Atrium Segmentation Research Based On Deep Learning

In recent years,using deep learning technology to assist in the diagnosis of medical images has become a hot research direction.According to relevant literature,cardiovascular diseases is an important factor causing higher mortality worldwide.How to improve the efficiency of doctors in the diagnosis of cardiovascular diseases and determine the treatment plan as soon as possible has also become a health problem that people pay more attention to.By using computer-aided methods to quickly process a large amount of cardiac images to speed up the diagnosis efficiency and accuracy,this is a work of great clinical significance.Previous studies on cardiac images used manual to delineate and analyze the contours of cardiac tissue structures(such as bi-ventricular and left atrium),and quantitatively analyze the functional indicators of tissue structures to determine whether the cardiac has developed a disease.In clinical applications,currently the radiologists are mainly used to manually delineate the bi-ventricular and the left atrium,which is time-consuming,boring and inefficient.The study aims to the segmentation of cardiac bi-ventricle and left atrium based on deep neural networks.It includes the following aspects:(1)Accurate segmentation of cardiac bi-ventricle from magnetic resonance images has a great significance to analyze and evaluate the function of the cardiovascular system.However,the complex structure of cardiac bi-ventricle image makes fully automatic segmentation as a well-known challenge.This dissertation proposes an improved end-to-end encoder-decoder network for cardiac bi-ventricle segmentation from the pixel level view.In the framework,it explicitly solves the high variability of complex cardiac structures through an improved encoder-decoder architecture which consists of Fire dilated modules and D-Fire dilated modules.This improved encoder-decoder architecture has the advantages of being capable of obtaining semantic task-aware representation and preserving fine-grained information.In addition,the method can dynamically capture potential spatiotemporal correlations between consecutive cardiac images through specially designed convolutional long short-term memory structure;it can simulate spatiotemporal contexts between consecutive frame images.The combination of these modules enables the entire network to get an accurate,robust segmentation result.The proposed method is evaluated on the 145 clinical subjects with leaveone-out cross-validation.The average dice metric is up to 0.96(left ventricle),0.89(myocardium),and 0.903(right ventricle).These results demonstrate the effectiveness and advantages of our method for cardiac bi-ventricle regions segmentation at the pixel-level,and it also reveals the proposed automated segmentation system can be embedded into the clinical environment to accelerate the quantification of cardiac bi-ventricle and expanded to volume analysis and regional wall thickness analysis and so on.(2)Left atrium segmentation plays a crucial role in the clinical analysis of atrial fibrillation,and visualization of atrial geometry can improve understanding and treatment of atrial fibrillation.However,most existing methods directly pass features in their networks,which may result in redundant information being passed to affect the final segmentation performance.In addition,they did not further consider the atrial visualization after segmentation,which led to a lack of understanding of the basic atrial anatomy.This dissertation proposes a new unified deep learning framework for simultaneous segmentation and visualization of the left atrium.First,a new dual-path model is proposed to enhance the expressiveness of cardiac images.A multi-scale context-aware module was designed to effectively handle the complex appearance and shape changes of the left atrium and associated pulmonary veins.The generated multi-scale features are fed back to the gated bi-directional message passing module to remove irrelevant information and extract useful features.Finally,through the deep supervision mechanism,the features are effectively combined to generate the final segmentation result and visualize the three-dimensional structure.The method is experimented on the 2018 left atrium segmentation challenge data set,which consists of 100 gadolinium enhancement magnetic resonance volumes.The results show that the segmentation results are in good agreement with the gold standard.The performance demonstrates the effectiveness and advantages of our network for the left atrium segmentation and visualization.Therefore,the proposed network could potentially improve the clinical diagnosis and treatment of atrial fibrillation.