SMALL BOWEL MUCOSA SEGMENTATION FOR FRAME CHARACTERIZATION IN VIDEOS OF ENDOSCOPIC CAPSULES

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Pinheiro, G (Author) Other; Coelho, P (Author) Other; Mourao, M (Author) Other; Salgado, M (Author) Other; Oliveira, HP (Author) FCUP; Cunha, A (Author) Other

Conference proceedings International

Title: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)

Pages: 83-86

16th IEEE International Symposium on Biomedical Imaging (ISBI)

Venice, ITALY, APR 08-11, 2019

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-R6V

DOI: 10.1109/isbi.2019.8759598

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4