Name
Title | ||
|---|---|---|
A Semi-Automated Method for Measurement of Left Ventricular Volumes in 3D Echocardiography. |
Abstract | ||
|---|---|---|
Segmentation of the left ventricle in echocardiography data currently poses a challenge, where delineation of the endocardial borders is a time consuming and difficult task. Though semi-automated and fully automated methods have been developed for left ventricular segmentation, they suffer from a number of drawbacks. These drawbacks include the dependence on large sets of training data and assumptions about the distribution of the intensities of the image. This paper proposes a novel volumetric segmentation algorithm based on an angular slicing approach for 3-D echocardiography scans and a diffeomorphic nonrigid registration method. The proposed method is fast, reproducible, and yields a volumetric segmentation with minimal user interaction. The algorithm was evaluated on 30 participants from the challenge on endocardial 3-D ultrasound segmentation dataset from the medical image computing and computer assisted interventions Challenge 2014. The proposed method yielded the following average distance metrics for the end diastolic volumes: 1) mean absolute distance of 2.36 mm, 2) Hausdorff distance of 8.25 mm, and 3) Dice score of 0.887. For the end systolic volumes, the following average distance metrics were obtained: 1) mean absolute distance of 2.33 mm, 2) Hausdorff distance of 8.95 mm, and 3) Dice score of 0.857. The following clinical metrics for the ejection fraction are reported: 1) modified correlation coefficient of 0.169, 2) bias in mL of -3.96 mL, and 3) standard deviation of 6.85 mL. The results demonstrate the robustness of the proposed volumetric segmentation approach. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2816340 | IEEE ACCESS |
Keywords | Field | DocType |
Image segmentation,image registration,echocardiography,left ventricle,ejection fraction | Correlation coefficient,Pattern recognition,Computer science,Segmentation,Image segmentation,Robustness (computer science),Medical image computing,Hausdorff distance,Artificial intelligence,Dice,Standard deviation,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krishnaswamy Deepa | 1 | 3 | 1.16 |
| Abhilash Rakkunedeth Hareendranathan | 2 | 0 | 2.70 |
| Tan Suwatanaviroj | 3 | 0 | 0.34 |
| Harald Becher | 4 | 105 | 9.60 |
| Noga, M. | 5 | 10 | 7.74 |
| Kumaradevan Punithakumar | 6 | 216 | 24.40 |