Volumetric parcellation of the right ventricle for regional geometric and functional assessment

03/18/2020
by   Gabriel Bernardino, et al.
33

In clinical practice, assessment of right ventricle (RV) is primarily done through its global volume, given it is a standardised measurement, and has a good reproducibility in 3D modalities such as MRI and 3D echocardiography. However, many illness produce regionalchanges and therefore a local analysis could provide a better tool for understanding and diagnosis of illnesses. Current regional clinical measurements are 2D linear dimensions, and suffer of low reproducibility due to the difficulty to identify landmarks in the RV, specially in echocardiographic images due to its noise and artefacts. We proposed an automatic method for parcellating the RV cavity and compute regional volumes and ejection fractions in three regions: apex, inlet and outflow. We tested the reproducibility in 3D echocardiographic images. We also present a synthetic mesh-deformation method to generate a groundtruth dataset for validating the ability of the method to capture different types of remodelling. Results showed an acceptable intra-observer reproduciblity (<10 but a higher inter-observer(>10 that the method was capable of assessing global dilatations, and local dilatations in the circumferential direction but not longitudinal elongations

READ FULL TEXT

page 4

page 13

page 14

page 15

page 23

page 24

page 25

research
02/05/2021

Reproducibility in Evolutionary Computation

Experimental studies are prevalent in Evolutionary Computation (EC), and...
research
02/15/2023

Clustering-Based Inter-Regional Correlation Estimation

A novel non-parametric estimator of the correlation between grouped meas...
research
06/30/2016

maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images

Supervoxel methods such as Simple Linear Iterative Clustering (SLIC) are...
research
09/06/2021

Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data

Graph neural networks (GNNs) have witnessed an unprecedented proliferati...

Please sign up or login with your details

Forgot password? Click here to reset