Computer Aided Diagnosis Of Carotid Artery Stenosis By Using Deep Learning

Carotid artery stenosis has currently become one of the killers endangering human health.Carotid artery stenosis can be divided into common carotid artery stenosis,internal carotid artery stenosis and external carotid artery stenosis,which require different stenosis degree calculation methods and give different health impacts.Doctors usually diagnose through digital subtraction angiography(DSA)to locate stenosis positions and calculate stenosis degrees.There are risks of missed diagnoses and misdiagnoses by doctors,and the accurate evaluation of stenosis degrees is also challenging.In recent years,the combination of artificial intelligence and medical treatment becomes feasible and effective.To improve the diagnosis efficiency and reduce the misdiagnosis rate,this thesis aims to develop new technique for computer aided diagnosis of carotid artery stenosis by using deep learning.The main research contents and contributions are summarized as follows:1.Image segmentations.Binary images are required for carotid artery stenosis.In this thesis,we focus on the stenosises of the main carotid artery.However,the branches of the carotid artery will affect the calculations of the widths of carotid arteries.By selecting the threshold value of each line for row binarization,and then erasing branches of the row binarization result,we can obtain the semantic segmentation label.After comparing the conventional semantic segmentation FCN series networks,Deeplab v3 network,the medical semantic segmentation U-Net network and CE-Net network,the CE-Net network is finally selected for the main carotid artery segmentation and the network is further optimized.2.Carotid artery stenosis locations.If the widths of carotid arteries are calculated in the whole binary image,adjacent carotid arteries may become the same artery after segmentation,and the width may increase.Additionally,there are still cases of carotid arteries overlapping and crossing in the image.As the binary images have weakened the flow direction information of carotid arteries,it is difficult to judge the position relationship of carotid arteries when overlapping and crossing happen.After the widths of carotid arteries are obtained,matching the widths to original carotid arteries are difficult.When some of them are matched correctly,quantities of width changes to locate stenosis positions are difficult to decide.In short,locating stenosis positions according to width comparisons is not easy to achieve.Therefore,it is better to locate the stenosis positions directly on the original image.The design of width comparisons is replaced by network learning features.After comparing accuracies of one-stage network RetinaNet and two-stage network Cascade R-CNN with different stenosis types,RetinaNet with stenosises divided into two types is finally selected as the detection scheme.3.Carotid artery stenosis degree calculations.We integrate the binary images segmented from the CE-Net and the detection scheme of the carotid artery stenosis locations to further calculate stenosis degrees,which reduces the calculation range and the possibility of carotid arteries overlapping and crossing.After finding edges of carotid arteries according to differences of pixels' values,widths of carotid arteries can be obtained.According to widths of the stenosis position and the normal position,the stenosis degree is calculated.4.System designs.The thesis designs a software,where the existing algorithms have been embedded.After importing carotid artery videos or images through the software interface,and sending them to the remote computer for processing,results are finally returned for aiding doctors to diagnose.