A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 6; pp. 2029 - 2040
• Main Authors: Jha, Debesh, Smedsrud, Pia H., Johansen, Dag, de Lange, Thomas, Johansen, Havard D., Halvorsen, Pal, Riegler, Michael A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Algorithms; Augmentation; Basale medisinske, odontologiske og veterinærmedisinske fag: 710; Basic medical, dental and veterinary science disciplines: 710; Cancer; Colon; Colonoscopy; Colorectal cancer; Colorectal carcinoma; Computer and Information Sciences; Computer architecture; conditional random field; Conditional random fields; Data- och informationsvetenskap (Datateknik); Datasets; Gastroenterologi och hepatologi; Gastroenterology and Hepatology; generalization; Hospitals; Image segmentation; Learning algorithms; Machine learning; Medical disciplines: 700; Medisinske Fag: 700; Performance enhancement; Physicians; polyp segmentation; Polyps; ResUNet; Segmentation; Task analysis; test-time augmentation; Testing time; Training; VDP
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2021.3049304

Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA). We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib Polyp DB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other state-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF and TTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model's performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist, 196 sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.