In this paper we present a novel approach for brain surfacec characterization based on convexity and concavity analysis of cortical surface mesh. Initially, volumetric Magnetic Resonance Images (MRI) data is processed...In this paper we present a novel approach for brain surfacec characterization based on convexity and concavity analysis of cortical surface mesh. Initially, volumetric Magnetic Resonance Images (MRI) data is processed to generate a discrete representation of cortical surface using low-level segmentation tools and Level-Sets method. Afterward, pipeline procedure for brain characterization/labeling is developed. The first characterization method is based on discrete curvature classification. This is consists on estimating curvature information at each vertex in the cortical surface mesh. The second method is based on transforming the brain surface mesh into Digital Elevation Model (DEM), where each vertex is designed by its space coordinates and geometric measures related to a reference surface. In other word, it consists on analyzing the cortical surface as a topological map or an elevation map where the ridge or crest lines represent cortical gyri and valley lines represents sulci. The experimental results have shown the importance of these characterization methods for the detection of significant details related to the cortical surface.展开更多
Background:To evaluate a fully automated vascular density(VD),skeletal density(SD)and fractal dimension(FD)method for the longitudinal analysis of retinal vein occlusion(RVO)eyes using projection-resolved optical cohe...Background:To evaluate a fully automated vascular density(VD),skeletal density(SD)and fractal dimension(FD)method for the longitudinal analysis of retinal vein occlusion(RVO)eyes using projection-resolved optical coherence tomography angiography(OCTA)images and to evaluate the association between these quantitative variables and the visual prognosis in RVO eyes.Methods:Retrospective longitudinal observational case series.Patients presenting with RVO to Creteil University Eye Clinic between October 2014 and December 2018 and healthy controls were retrospectively evaluated.Group 1 consisted of central RVO(CRVO)eyes,group 2 consisted of eyes with branch RVO(BRVO)and group 3 of healthy control eyes.OCTA acquisitions(AngioVue RTVue XR Avanti,Optovue,Inc.,Freemont,CA)were performed at baseline and last follow up visit.VD,SD,and FD analysis were computed on OCTA superficial and deep vascular complex(SVC,DVC)images at baseline and final follow up using an automated algorithm.Logistic regression was performed to find if and which variable(VD,SD,FD)was predictive for the visual outcome.Results:Forty-one eyes,of which 21 consecutive eyes of 20 RVO patients(13 CRVO in group 1,8 BRVO in group 2),and 20 eyes of 20 healthy controls were included.At the level of SVC,VD and FD were significantly lower in RVO eyes compared to controls(P<0.0001 and P=0.0008 respectively).Best-corrected visual acuity(BCVA)at last follow-up visit was associated with baseline VD(P=0.013),FD(P=0.016),and SD(P=0.01)at the level of the SVC,as well as with baseline FD at the DVC level(P=0.046).Conclusions:Baseline VD,SD,and FD are associated with the visual outcome in RVO eyes.These parameters seem valuable biomarkers and may help improve the evaluation and management of RVO patients.展开更多
文摘In this paper we present a novel approach for brain surfacec characterization based on convexity and concavity analysis of cortical surface mesh. Initially, volumetric Magnetic Resonance Images (MRI) data is processed to generate a discrete representation of cortical surface using low-level segmentation tools and Level-Sets method. Afterward, pipeline procedure for brain characterization/labeling is developed. The first characterization method is based on discrete curvature classification. This is consists on estimating curvature information at each vertex in the cortical surface mesh. The second method is based on transforming the brain surface mesh into Digital Elevation Model (DEM), where each vertex is designed by its space coordinates and geometric measures related to a reference surface. In other word, it consists on analyzing the cortical surface as a topological map or an elevation map where the ridge or crest lines represent cortical gyri and valley lines represents sulci. The experimental results have shown the importance of these characterization methods for the detection of significant details related to the cortical surface.
文摘Background:To evaluate a fully automated vascular density(VD),skeletal density(SD)and fractal dimension(FD)method for the longitudinal analysis of retinal vein occlusion(RVO)eyes using projection-resolved optical coherence tomography angiography(OCTA)images and to evaluate the association between these quantitative variables and the visual prognosis in RVO eyes.Methods:Retrospective longitudinal observational case series.Patients presenting with RVO to Creteil University Eye Clinic between October 2014 and December 2018 and healthy controls were retrospectively evaluated.Group 1 consisted of central RVO(CRVO)eyes,group 2 consisted of eyes with branch RVO(BRVO)and group 3 of healthy control eyes.OCTA acquisitions(AngioVue RTVue XR Avanti,Optovue,Inc.,Freemont,CA)were performed at baseline and last follow up visit.VD,SD,and FD analysis were computed on OCTA superficial and deep vascular complex(SVC,DVC)images at baseline and final follow up using an automated algorithm.Logistic regression was performed to find if and which variable(VD,SD,FD)was predictive for the visual outcome.Results:Forty-one eyes,of which 21 consecutive eyes of 20 RVO patients(13 CRVO in group 1,8 BRVO in group 2),and 20 eyes of 20 healthy controls were included.At the level of SVC,VD and FD were significantly lower in RVO eyes compared to controls(P<0.0001 and P=0.0008 respectively).Best-corrected visual acuity(BCVA)at last follow-up visit was associated with baseline VD(P=0.013),FD(P=0.016),and SD(P=0.01)at the level of the SVC,as well as with baseline FD at the DVC level(P=0.046).Conclusions:Baseline VD,SD,and FD are associated with the visual outcome in RVO eyes.These parameters seem valuable biomarkers and may help improve the evaluation and management of RVO patients.
