# Automated polyp detection in colon capsule endoscopy

arXiv: https://arxiv.org/abs/1305.1912 · HTML: https://mlsanity.com/paper/1305.1912/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2013.

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

- [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)
- [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)
- [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)
- [Duplex Contextual Relation Network for Polyp Segmentation](https://mlsanity.com/paper/2103.06725/) (2021)
- [Edge-aware Feature Aggregation Network for Polyp Segmentation](https://mlsanity.com/paper/2309.10523/) (2023)
- [Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video](https://mlsanity.com/paper/2101.04240/) (2021)
- [MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer](https://mlsanity.com/paper/2401.12439/) (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)
- [Federated Foundation Model for GI Endoscopy Images](https://mlsanity.com/paper/2505.24108/) (2025)
- [Enhancing Polyp Segmentation via Encoder Attention and Dynamic Kernel Update](https://mlsanity.com/paper/2509.23502/) (2025)
- [Assisted Refinement Network Based on Channel Information Interaction for Camouflaged and Salient Object Detection](https://mlsanity.com/paper/2512.11369/) (2025)
- [Deep Learning-based Biological Anatomical Landmark Detection in Colonoscopy Videos](https://mlsanity.com/paper/2108.02948/) (2021)
- [Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2110.09110/) (2021)
- [Colonoscopy Landmark Detection using Vision Transformers](https://mlsanity.com/paper/2209.11304/) (2022)
- [SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation](https://mlsanity.com/paper/2406.10200/) (2024)
- [Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study](https://mlsanity.com/paper/2101.04034/) (2021)
- [Unsupervised Shot Boundary Detection for Temporal Segmentation of Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2110.09067/) (2021)
- [Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy](https://mlsanity.com/paper/2211.02416/) (2022)
- [FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies](https://mlsanity.com/paper/2309.05987/) (2023)
- [Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model](https://mlsanity.com/paper/2410.01210/) (2024)
- [Hybrid(Transformer+CNN)-based Polyp Segmentation](https://mlsanity.com/paper/2508.09189/) (2025)
- [PVTAdpNet: Polyp Segmentation using Pyramid vision transformer with a novel Adapter block](https://mlsanity.com/paper/2509.23751/) (2025)

## Abstract (arXiv)

Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy (CCE) is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame. The geometrical analysis is based on a segmentation of an image with the help of a mid-pass filter. The features extracted by the segmentation procedure are classified according to an assumption that the polyps are characterized as protrusions that are mostly round in shape. Thus, we use a best fit ball radius as a decision parameter of a binary classifier. We present a statistical study of the performance of our approach on a data set containing over 18,900 frames from the endoscopic video sequences of five adult patients. The algorithm demonstrates a solid performance, achieving 47% sensitivity per frame and over 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by a human operator.