Name
Title | ||
|---|---|---|
Analysis Of Mitochondrial Shape Dynamics Using Large Deformation Diffeomorphic Metric Curve Matching |
Abstract | ||
|---|---|---|
Mitochondrial shape changes are essential to mitochondrial functions. Quantification of mitochondrial shape changes is essential to understanding related physiology and disease mechanisms. In this study, we proposed a new automated pipeline for quantifying the shape changing patterns of mitochondria in the framework of large deformation diffeomorphic metric mapping for curve. We validated the accuracy of our pipeline on 32 mitochondria data, each having 6 sequential time-lapse frames. The contour of each mitochondrion is modeled by a curve consisting of a set of landmark points ranging from 39 to 358, with the moving distance between every two consecutive frames quantified for each localized point. The sensitivity of the proposed pipeline, with respect to different curve discretization, was investigated, with high robustness established. In addition, we quantified the uncertainty level of the proposed pipeline using 10 fixed mitochondria data with 6 time frames as well, with the mean between-frame moving distance found to be smaller than 28 nm for a majority of the 10 fixed mitochondria data. This indicates that the proposed pipeline has a very low level of uncertainty. The encouraging results from this work suggest that the proposed pipeline is potentially a powerful tool for quantifying shape dynamics, both globally and locally, of a variety of cellular components. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8037748 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Discretization,Biological system,Computer science,Robustness (computer science),Ranging,Artificial intelligence,Deformation (mechanics),Diffeomorphism,Topology,Computer vision,Large deformation diffeomorphic metric mapping,Shape dynamics,Landmark | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
6 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huilin Yang | 1 | 0 | 0.34 |
| Jing Wang | 2 | 507 | 93.00 |
| Haiyun Tang | 3 | 0 | 0.34 |
| Qinle Ba | 4 | 0 | 1.35 |
| Ge Yang | 5 | 18 | 5.89 |
| Xiaoying Tang | 6 | 8 | 8.79 |