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

  title={A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation},
  author={Debesh Jha and Pia Helen Smedsrud and Dag Johansen and Thomas de Lange and H{\aa}vard Dagenborg Johansen and P. Halvorsen and M. Riegler},
  journal={IEEE Journal of Biomedical and Health Informatics},
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… Expand
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Self-Supervised U-Net for Segmenting Flat and Sessile Polyps
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PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation
  • Abhishek Srivastava, S. Chanda, +4 authors U. Pal
  • Computer Science, Engineering
  • ArXiv
  • 2021
Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of aExpand
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers
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ResUNet++: An Advanced Architecture for Medical Image Segmentation
ResUNet++ is proposed, which is an improved ResUNet architecture for colonoscopic image segmentation, which significantly outperforms U-Net and Res UNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores. Expand
Training Data Enhancements for Robust Polyp Segmentation in Colonoscopy Images
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Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge
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A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
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Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images
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Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images
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Colorectal polyp segmentation using a fully convolutional neural network
  • Qiaoliang Li, Guangyao Yang, +7 authors Tianfu Wang
  • Computer Science
  • 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
  • 2017
A new, end-to-end fully convolutional neural network structure for segmenting colorectal polyps, which can directly output a prediction map of the same size as the original image of the input network is proposed. Expand