A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation
| Autor: | Thomas de Lange, Pål Halvorsen, Michael Riegler, Pia H. Smedsrud, Dag Johansen, Debesh Jha, Håvard D. Johansen |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Conditional random field Computer science Colorectal cancer Test time augmentation Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Colonoscopy Generalizations Colonic Polyps 02 engineering and technology Conditional random fields 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Health Information Management ResUNet++ 0202 electrical engineering electronic engineering information engineering medicine Polyp segmentations Humans Segmentation Diagnosis Computer-Assisted Electrical and Electronic Engineering Artificial neural network medicine.diagnostic_test business.industry VDP::Medisinske Fag: 700::Basale medisinske odontologiske og veterinærmedisinske fag: 710 Pattern recognition Image segmentation Gold standard (test) medicine.disease VDP::Medical disciplines: 700::Basic medical dental and veterinary science disciplines: 710 Computer Science Applications Colorectal Polyp 020201 artificial intelligence & image processing Artificial intelligence Neural Networks Computer business Algorithms Biotechnology |
| Zdroj: | IEEE journal of biomedical and health informatics |
| ISSN: | 2168-2208 2168-2194 |
| Popis: | 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. Accepted at IEEE Journal of BioMedical and Health Informatics |
| Databáze: | OpenAIRE |
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