Name
Title | ||
|---|---|---|
Small Bowel Mucosa Segmentation for Frame Characterization in Videos of Endoscopic Capsules |
Abstract | ||
|---|---|---|
Endoscopic capsules are vitamin-sized devices that leverage from a small wireless camera to create 8 to 10 hour videos of the patients' entire digestive tract, still being the leading tool to diagnose small bowel diseases. The revision of the produced videos is a very time-consuming task, currently conducted manually and frame-by-frame by an expert. Since endoscopic videos usually contain a considerable amount of frames where the mucosa is not clearly visible, the segmentation of the informative regions is a vital component to reduce the necessary time to review each exam. In this work, a CNN encoder-decoder architecture is applied to segment informative regions in small bowel frames of videos of endoscopic capsules. The network was trained and tested with a dataset of 2,929 manually annotated images, achieving a 91.2% Dice coefficient and 83.9% IoU. Furthermore, a video-wise analysis based on the amount of informative pixels in each frame is done. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISBI.2019.8759598 | 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) |
Keywords | Field | DocType |
Endoscopic capsule,mucosa segmentation,informative frames,encoder-decoder,deep learning | Computer vision,Encoder decoder,Pattern recognition,Segmentation,Computer science,Sørensen–Dice coefficient,Entire digestive tract,Artificial intelligence,Pixel,Deep learning,Wireless camera | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3642-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gil Pinheiro | 1 | 0 | 0.34 |
| Paulo Jorge Simães Coelho | 2 | 0 | 0.34 |
| Mariana Mourão | 3 | 0 | 0.34 |
| Marta Salgado | 4 | 0 | 0.68 |
| Hélder P. Oliveira | 5 | 63 | 13.99 |
| António Cunha | 6 | 0 | 0.34 |