Research On Intelligent Analysis And Processing Algorithm Of Coronary Angiography

In recent years,the incidence rate of cardiovascular diseases in China has continued to increase,with the current number of patients reaching 330 million.As a main treatment method for cardiovascular diseases,the surgery of Percutaneous Coronary Intervention heavily relies on the accurate interpretation of angiography images.However,the interpretation accuracy is significantly affected by individual experience and subjective judgments of doctors,which will substantially interfere the selection of optimal treatment strategies.Therefore,it is urgent to investigate the intelligent analysis and processing algorithms of coronary angiography images based on deep learning.The purpose is to achieve automatic,timely,and accurate assessment of patients’ conditions,thereby improving the treatment level of hospitals across China.The diagnosis and treatment of cardiovascular diseases require comprehensive consideration of multiple complex conditions,such as vascular stenosis,calcification,Chronic Total Occlusion(CTO)and thrombosis.Meanwhile,angiography images are characterized by narrow blood vessels but dominant interference information.Therefore,our proposed intelligent analysis and processing algorithms should be capable of handling all interference and noise in images,facilitating detection of lesion areas,segmentation of blood vessels,classification of lesion morphology and quantification of lesion degree for multiple complex diseases simultaneously.This thesis first constructs a coronary artery tree(CAT)containing multidimensional information such as vessel diameter and coordinates,which is the basis of vascular infrastructure recognition and stenosis degree quantification measured by automatically Quantitative Coronary Analysis(QCA).Then,we improve the built CAT via semantic segmentation of vessels in order to facilitate the segment locating of multiple lesions.Furthermore,this thesis explores the lesion area detection and morphology classification of multiple diseases including CTO,calcification,thrombosis,etc.The main work and innovation of this thesis can be summarized as following.(1)CAT construction and automatic QCA based on deep learning.This thesis proposes a fully automatic mechanism of QCA,aiming to achieve precise evaluation of vascular stenosis while minimizing time cost and manual operations,which comprises three stages of vessel segmentation,CAT construction and QCA operations.First,in order to effectively deal with the impacts of interference and noise in images,the vessel segmentation stage employs an edge-aware binary segmentation network for coronary arteries to extract high-level global information,lowlevel detailed information and important edge information,thereby accurately acquiring the interested vessel areas.Then,the CAT construction stage uses a ternary segmentation model based deep learning to assist in root node locating,along with parallel thinning and optimization algorithms,thereby realizing the automatic construction of CAT.Finally,the QCA operation stage utilizes techniques such as Gaussian smoothing,branch separation,reference diameter fitting,stenosis quantification and detection to precisely measure the stenosis degree,location,length,etc.Experiment results demonstrate that the proposed automatic QCA mechanism can not only achieve the same measurement accuracy as expert outcomes,but also significantly reduce the diagnosis time.(2)Construction and optimization of semantic CAT based on continuous frame images.Based on the forementioned CAT structure,we utilize semantic segmentation of vessels to add semantic information to the constructed CAT,while using continuous frame images to further improve the accuracy of CAT structure,in order to facilitate the segment locating of multiple lesions.The proposed mechanism of semantic CAT mainly refers to multiclass semantic segmentation of vascular segments,along with the construction and optimization of semantic CAT based on keyframe and continuous-frame images,respectively.First,a multi-class semantic segmentation algorithm for vascular segments based on generative deep learning networks is proposed,which leverages the image translation theory to accurately segment and recognize twenty types of vascular segments.Then,an automatic keyframe selection algorithm is proposed to improve the automatic construction of CAT,followed by adding the semantic information of vascular segments to the CAT.Finally,the semantic CAT will be further optimized from two aspects according to continuous frame images.On one hand,we use tensor voting techniques to generate the probability map of features,and then use the shortest path algorithm to fix up interruptions on vessels.On the other hand,we utilize clustering algorithms to identify abnormal branching nodes according to continuous frame images,and then remove redundant vascular branches.Experimental results show that the semantic CAT optimized based on continuous frame images is able to recognize vascular structures more accurately,especially for the structure of branch vessels.(3)Lesion detection and classification of coronary angiography images based on deep learning.This thesis proposes a detection framework based on deep neural networks for coronary angiography images,aiming to realize automatic detection of five types of lesion morphologies,including stenosis,calcification,thrombus,CTO and dissection.The proposed algorithm uses a region proposal network to recommend areas where the above lesions may occur,while utilizing a feature pyramid network to extract features of four different scales to cater for lesions of different scales,by which we can realize lesion classifications and obtain region coordinates.Specially,due to the morphology of CTO stumps has dominant impacts on the success rate of surgeries,this thesis proposes a classification network based on deep learning to further distinguish the stump morphology.In details,we use a residual network to extract features of lesion morphologies,leverage a feature pyramid approach to strengthen the abilities of extracting features from small target regions,and utilize a reciprocative learning algorithm for the attention regularization of classification losses,with the objective of raising classifier’s attention on the features of lesion stump areas and thus increasing the accuracy of CTO stump morphology classification.Experimental results show that the proposed detection and classification algorithms have high precision,recall,and F1 scores on the test dataset,demonstrating the effectiveness in lesion morphology detection and CTO stump classification.In summary,this thesis achieves the construction and optimization of semantic CAT,automatic QCA for stenosis quantification and detection of multiple lesion morphologies,successfully enabling the intelligent recognition of five types of lesions including stenosis,calcification,thrombus,CTO and dissection.Based on the above contents,we further realize the automatic estimation and quantification of patients’ overall condition,and then develop an AI-assisted diagnosis and treatment system to aid doctors in selecting the optimal strategy of surgeries,thus improving the treatment level of cardiovascular diseases in hospitals across China.