# Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

arXiv: https://arxiv.org/abs/2011.07631 · HTML: https://mlsanity.com/paper/2011.07631/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (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)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy](https://mlsanity.com/paper/2105.07467/) (2021)
- [GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation](https://mlsanity.com/paper/2111.10614/) (2021)
- [Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy](https://mlsanity.com/paper/2107.02319/) (2021)
- [Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation](https://mlsanity.com/paper/2104.00850/) (2021)
- [Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy](https://mlsanity.com/paper/2104.01268/) (2021)
- [Deep ensembles in bioimage segmentation](https://mlsanity.com/paper/2112.12955/) (2021)
- [PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation](https://mlsanity.com/paper/2111.10618/) (2021)
- [Applied Deep Learning to Identify and Localize Polyps from Endoscopic Images](https://mlsanity.com/paper/2301.09219/) (2023)
- [Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction](https://mlsanity.com/paper/2304.03171/) (2023)
- [Gastrointestinal Mucosal Problems Classification with Deep Learning](https://mlsanity.com/paper/2307.16198/) (2023)

## Abstract (arXiv)

Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localisation, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localisation task. Likewise, the proposed ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.