Name
Abstract | ||
|---|---|---|
A statistical appearance model of blood vessels based on variational autoencoder (VAE) is well adapted to image intensity variations. However, images reconstructed with such a statistical model may have topological defects, such as loss of bifurcation and creation of undesired hole. In order to build a 3D anatomical model of blood vessels, we incorporate topological prior into the statistical modeling. Qualitative and quantitative results on 2567 real CT volume patches and on 10000 artificial ones show the efficiency of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87444-5_11 | INTERPRETABILITY OF MACHINE INTELLIGENCE IN MEDICAL IMAGE COMPUTING, AND TOPOLOGICAL DATA ANALYSIS AND ITS APPLICATIONS FOR MEDICAL DATA |
Keywords | DocType | Volume |
Statistical model, Topological data analysis, Variational autoencoder, Vasculature | Conference | 12929 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuki Saeki | 1 | 0 | 0.34 |
| Atsushi Saito | 2 | 21 | 6.19 |
| Jean Cousty | 3 | 0 | 0.34 |
| Yukiko Kenmochi | 4 | 252 | 33.29 |
| Akinobu Shimizu | 5 | 35 | 7.62 |