Name
Title | ||
|---|---|---|
A Novel Approach Based On Spatio-Temporal Features And Random Forest For Scar Detection Using Cine Cardiac Magnetic Resonance Images |
Abstract | ||
|---|---|---|
Aim. To identify the presence of scar tissue in the left ventricle from Gadolinium (Gd)-free magnetic resonance cine sequences using a learning-based approach relying on spatio-temporal features. Methods. The spatial and temporal features were extracted using local binary patterns from (i) cine end-diastolic frame and (ii) two parametric images of amplitude and phase wall motion, respectively, and classified with Random Forest. Results. When tested on 328 cine sequences from 40 patients, a recall of 70% was achieved, improving significantly the classification resulting from spatial and temporal features processed separately. Conclusions. The proposed approach showed promising results, paving the way for scar identification from Gd-free images. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.22489/CinC.2020.050 | 2020 COMPUTING IN CARDIOLOGY |
DocType | ISSN | Citations |
Conference | 2325-8861 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Moccia | 1 | 0 | 0.68 |
| Alessandro Cagnoli | 2 | 0 | 0.34 |
| Chiara Martini | 3 | 0 | 0.34 |
| Giuseppe Moscogiuri | 4 | 0 | 0.34 |
| Mauro Pepi | 5 | 0 | 0.34 |
| Emanuele Frontoni | 6 | 248 | 47.04 |
| Gianluca Pontone | 7 | 0 | 0.34 |
| Enrico Gianluca Caiani | 8 | 0 | 0.34 |