Heart Image Segmentation Based On Neural Network

Currently,the cardiovascular disease is one of the main causes of human non-accidental deaths worldwide according to the World Health Organization.In developing nations and rural areas,difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities.The heart is the central organ of the cardiovascular system,and obtaining the physiological information of the heart is the key for the diagnosis and treatment of cardiovascular disease.CT imaging technology has the advantages of fast imaging speed,clear imaging and so on.It is the common means of cardiac examination.The segmentation of CT image is important for the diagnosis and treatment of cardiovascular disease.However,the heart is a solid organ,the common fault image sequence is difficult to completely show the physiological information of the heart,and for computer-aided diagnosis,and interventional therapy guidance,cardiac surgery,and other technologies usually need to get a complete cardiac anatomy.Three-dimensional visualization of medical images is the main means to find out the anatomy of the heart,while image segmentation is the database to realize the visualization of human organs.For the segmentation of cardiac images,researchers at national and international have proposed a variety of segmentation methods,but most of the algorithms focus on the segmentation of the atria and ventricles,and difficult to meet the requirements of the whole heart segmentation.At the same time,due to the heart of the pulse and blood flow process is easily produce artifacts,border weakening,and other issues to CT images,affecting the heart wall segmentation effect.Based on the segmentation and recognition of medical images,the thesis presents a new method based on convolutional neural network and image prominence,which is based on the difference between the cardiac images and other tissues in the slices is obvious,and there is a high similarity between adjacent slices in the cardiac CT image sequence.The main contents and innovations of this thesis are as follows:1.The segmentation task is decomposed into two parts:location and segmentation,and using convolution neural network and stack noise reduction self-coding network to achieve the localization and segmentation.Constructed the convolution neural network based on the excellent performance in image classification and target recognition,achieved the positioning function of the heart in the image.Then,the image of the original heart CT is cut and the partial non-target area is removed by the positioning result.Besides constructed a stacked denoising autoencoder,and trained it by manually dividing the image to achieve the classification and recognition of the pixels belonging to the heart tissue in the CT image of the heart.Finally,the segmentation of the cardiac image is completed based on the classification result.2.Compared the results of this segmentation algorithm with the artificial segmentation results,and got the quantitative evaluation of these two results.Finally,visualized the segmentation results with two kinds of surface rendering and volume rendering visualization algorithms.