Name
Title | ||
|---|---|---|
Individual muscle segmentation in MR images: A 3D propagation through 2D non-linear registration approaches |
Abstract | ||
|---|---|---|
Manual and automated segmentation of individual muscles in magnetic resonance images have been recognized as challenging given the high variability of shapes between muscles and subjects and the discontinuity or lack of visible boundaries between muscles. In the present study, we proposed an original algorithm allowing a semi-automatic transversal propagation of manually-drawn masks. Our strategy was based on several ascending and descending non-linear registration approaches which is similar to the estimation of a Lagrangian trajectory applied to manual masks. Using several manually-segmented slices, we have evaluated our algorithm on the four muscles of the quadriceps femoris group. We mainly showed that our 3D propagated segmentation was very accurate with an averaged Dice similarity coefficient value higher than 0.91 for the minimal manual input of only two manually-segmented slices. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8036826 | 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Algorithms,Imaging, Three-Dimensional,Magnetic Resonance Imaging,Quadriceps Muscle | Computer vision,Nonlinear system,Lagrangian,Segmentation,Computer science,Discontinuity (linguistics),Transversal (geometry),Artificial intelligence,Dice,Trajectory | Conference |
Volume | ISSN | ISBN |
2017 | 1557-170X | 978-1-5090-2810-8 |
Citations | PageRank | References |
2 | 0.40 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Augustin Ogier | 1 | 2 | 0.40 |
| Michaël Sdika | 2 | 81 | 9.17 |
| Alexandre Foure | 3 | 2 | 0.74 |
| Arnaud Le Troter | 4 | 7 | 1.51 |
| David Bendahan | 5 | 2 | 0.74 |