# Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network

arXiv: https://arxiv.org/abs/1802.00368 · HTML: https://mlsanity.com/paper/1802.00368/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2018.

## Built on by (most cited first, 30 of 33)

- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021)
- [Progressively Normalized Self-Attention Network for Video Polyp Segmentation](https://mlsanity.com/paper/2105.08468/) (2021)
- [HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS](https://mlsanity.com/paper/2101.07172/) (2021)
- [Adaptive Context Selection for Polyp Segmentation](https://mlsanity.com/paper/2301.04799/) (2023)
- [A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation](https://mlsanity.com/paper/2107.12435/) (2021)
- [Lesion-aware Dynamic Kernel for Polyp Segmentation](https://mlsanity.com/paper/2301.04904/) (2023)
- [Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation](https://mlsanity.com/paper/2105.00999/) (2021)
- [Camouflaged Object Detection via Context-aware Cross-level Fusion](https://mlsanity.com/paper/2207.13362/) (2022)
- [Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge](https://mlsanity.com/paper/2202.12031/) (2022)
- [TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing](https://mlsanity.com/paper/2303.07428/) (2023)
- [Duplex Contextual Relation Network for Polyp Segmentation](https://mlsanity.com/paper/2103.06725/) (2021)
- [Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy](https://mlsanity.com/paper/2105.07467/) (2021)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations](https://mlsanity.com/paper/2212.03498/) (2022)
- [Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation](https://mlsanity.com/paper/2110.01939/) (2021)
- [Segmenting Medical Images: From UNet to Res-UNet and nnUNet](https://mlsanity.com/paper/2407.04353/) (2024)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [BoxShrink: From Bounding Boxes to Segmentation Masks](https://mlsanity.com/paper/2208.03142/) (2022)
- [Multi-scale Information Sharing and Selection Network with Boundary Attention for Polyp Segmentation](https://mlsanity.com/paper/2405.11151/) (2024)
- [Cooperation Learning Enhanced Colonic Polyp Segmentation Based on Transformer-CNN Fusion](https://mlsanity.com/paper/2301.06892/) (2023)
- [Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions](https://mlsanity.com/paper/2306.05912/) (2023)
- [Multi-Layer Dense Attention Decoder for Polyp Segmentation](https://mlsanity.com/paper/2403.18180/) (2024)
- [PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics](https://mlsanity.com/paper/2502.19037/) (2025)
- [Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images](https://mlsanity.com/paper/2107.00418/) (2021)
- [Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study](https://mlsanity.com/paper/2101.04034/) (2021)
- [Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy](https://mlsanity.com/paper/2211.02416/) (2022)
- [BetterNet: An Efficient CNN Architecture with Residual Learning and Attention for Precision Polyp Segmentation](https://mlsanity.com/paper/2405.04288/) (2024)

## Abstract (arXiv)

Colorectal cancer is a one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper we proposed a polyp segmentation method based on convolutional neural network. Performance of the method is enhanced by two strategies. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform an effective post processing on the probability map that is produced by the network. Evaluation of the proposed method using the CVC-ColonDB database shows that our proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods.