https://www.ncbi.nlm.nih.gov/pubmed/32048018
Surg Endosc. 2020 Feb 11. doi: 10.1007/s00464-019-07240-9. [Epub ahead of print]
Polyp fingerprint: automatic recognition of colorectal polyps' unique features.
García-Rodríguez A1, Bernal J2, Sánchez FJ2, Córdova H1, Garcés Durán R1, Rodríguez de Miguel C1, Fernández-Esparrach G3.
Author information
1Endoscopy Unit, Gastroenterology Department, Hospital Clínic, IDIBAPS, CIBEREHD, University of Barcelona, Barcelona, Spain.2Computer Science Department, Universitat Autònoma de Barcelona and Computer Vision Center, Barcelona, Spain.3Endoscopy Unit, Gastroenterology Department, Hospital Clínic, IDIBAPS, CIBEREHD, University of Barcelona, Barcelona, Spain. mgfernan@clinic.cat.
Abstract
BACKGROUND:
Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('polyp fingerprint').
METHODS:
A machine learning technique called Bag of Words was used to describe each endoscopic image containing a polyp in a unique way. The system was tested with 243 white light images belonging to 99 different polyps (for each polyp there were at least two images representing it in two different temporal moments). Images were acquired in routine colonoscopies at Hospital Clínic using high-definition Olympus endoscopes. The method provided for each image the closest match within the dataset.
RESULTS:
The system matched another image of the same polyp in 221/243 cases (91%). No differences were observed in the number of correct matches according to Paris classification (protruded: 90.7% vs. non-protruded: 91.3%) and size (< 10 mm: 91.6% vs. > 10 mm: 90%).
CONCLUSIONS:
A CBIR system can match accurately two images containing the same polyp, which could be a helpful aid for polyp image recognition.
KEYWORDS:
Artificial intelligence; Colorectal polyps; Content-based image retrieval
PMID: 32048018 DOI: 10.1007/s00464-019-07240-9
