Three Dimensional Quantitative Analysis Method Of Vascular Networks Based On Micro-CT Imaging

The main function of blood vessels in vertebrates is to provide each cell of surrounding tissues with oxygen and nutrients,meanwhile take away metabolic wastes.The dysfunction of vascular networks is closely related to the occurrence and development of pathological diseases,such as tumor,atherosclerosis,cognitive disease,retinal vascular disease and peripheral artery disease.Therefore,the quantitative analysis of vascular networks is of great clinical and preclinical importance in the diagnosis,treatment and prognosis of these vascular related diseases.Based on high resolution micro-computed tomography(micro-CT)imaging,this dissertation firstly discussed the segmentation of three dimensional(3D)vascular networks.Afterwards,this dissertation studied different quantitative analysis methods for 3D vascular networks,where the key problem was how to select appropriate vascular parameters in different quantitative applications.The main contributions of this dissertation could be summarized as follows:1.This dissertation proposed an automatic segmentation method for bones and blood vessels from murine hindlimbs,which included three importance modules.(1)To eliminate the interference of bones on the segmentation of blood vessels,we employed Split Bregman method to segment bones in the first place.(2)We proposed an edge extension strategy to cope with the false positive effect of the vesselness method on the sharp boundaries of hindlimb after the removal of bones.Then we segmented the blood vessels using the vesselness method combined with multiscale bi-Gaussian filtering.(3)We reconnected the broken blood vessels after segmentation based on centerline and morphological dilation.The experiments demonstrated that our proposed method was suitable for the segmentation of bones and blood vessels in murine hindlimbs.For the segmentation of bones,the Split Bregman method improved the distinguishability between bones and blood vessels,since both the intensity information and the geometrical size were exploited.For the segmentation of blood vessels,vesselness method with the edge extension strategy eliminated the false positive effect on the non-vascular sharp boundaries.After segmentation,the proposed vascular connection method based on centerline and morphological dilation could reconnect the broken blood vessels without affecting the non-broken ones.2.To solve the multicollinearity issue and unequal contribution of vascular parameters for the quantification of angiogenesis,we developed a quantification evaluation method of vascular parameters for angiogenesis based on in vivo micro-CT imaging of hindlimb ischemic model mice.Taking vascular volume as the ground truth parameter,nine vascular parameters(including vascular area,degree of anisotropy,connectivity density,vascular length,vascular thickness,vascular junction,junction degree,segment number,and segment tortuosity)were firstly assembled into sparse principal components(PCs)to reduce the multicolinearity issue.Aggregated boosted trees(ABTs)were then employed to analyze the importance of vascular parameters for the quantification of angiogenesis via the loadings of sparse PCs.The results demonstrated that vascular volume was mainly characterized by vascular area,vascular junction,connectivity density,segment number and vascular length,which indicated they were the key vascular parameters for the quantification of angiogenesis.The proposed quantitative evaluation method was compared with both the ABTs directly using the nine vascular parameters and Pearson correlation,which were consistent.In contrast to the ABTs directly using the vascular parameters,the proposed method can select all the key vascular parameters simultaneously,because all the key vascular parameters were assembled into the sparse PCs with the highest relative importance.3.In previous quantitative studies of vascular networks,neither the sensitivity analysis of the vascular parameters at multiple resolutions,nor the distinguishability estimation of vascular parameters from different data groups has been discussed.In this dissertation,we proposed a quantitative analysis method of vascular parameters for vascular networks of multi-resolution,by analyzing the sensitivity of vascular parameters at multiple resolutions and estimating the distinguishability of vascular parameters from different data groups.Combining the sensitivity and distinguishability,we designed a hybrid formulation to estimate the integrated performance of vascular parameters in a multi-resolution framework.Among the vascular parameters,degree of anisotropy and junction degree were two insensitive parameters that were nearly irrelevant with resolution degradation;vascular area,connectivity density,vascular length,vascular junction and segment number were five parameters that could better distinguish the vascular networks from different groups and abide by the ground truth.Vascular area,connectivity density,vascular length and segment number were not only insensitive to multi-resolution,but could also better distinguish vascular networks from different groups,which provided a guidance for the quantification of the vascular networks in multi-resolution frameworks.This dissertation focuses on the quantitative analysis of murine vascular networks,but the method can readily be applied to the quantitative study of vascular networks in tomor,heart,brain,and retina.Therefore,the proposed quantitative analysis method can potentially provide a more accurate evaluation technique in the diagnosis and prognosis for tumor,atherosclerosis,cognitive disease,retinal vascular disease and peripheral artery disease.