Name
Title | ||
|---|---|---|
Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets. |
Abstract | ||
|---|---|---|
•Fully-automated, real-time catheter and guidewire segmentation in fluoroscopy using CNNs.•Two-stage training strategy based on transfer learning technique, using synthetic images with predefined labelled segmentation.•Methods to reduce the need of manual pixel-level labelling to facilitate the development of CNN models for semantic segmentation, especially in the medical field.•Lightweight CNN model with a decreased number of network parameters which results in more efficient training and faster run times (84% reduction in testing time compared to the state-of-the-art). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.cmpb.2020.105420 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
Catheter segmentation,Deep learning,Fluoroscopy,Transfer learning | Journal | 192 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marta Gherardini | 1 | 0 | 0.34 |
| Evangelos B Mazomenos | 2 | 98 | 11.86 |
| Arianna Menciassi | 3 | 768 | 138.57 |
| Danail Stoyanov | 4 | 792 | 81.36 |