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

• URL: http://arxiv.org/abs/2107.12435v1
• Date: Mon, 26 Jul 2021 18:55:58 GMT
• Title: A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
• Authors: Debesh Jha, Pia H. Smedsrud, Dag Johansen, Thomas de Lange, H{\aa}vard D. Johansen, P{\aa}l Halvorsen, and Michael A. Riegler
• Abstract summary: Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer.
• Score: 0.7224497621488285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 and test-time augmentation. 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.

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