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

TL;DR

This paper benchmarks recent deep learning methods for real-time polyp detection, localization, and segmentation in colonoscopy images, highlighting ColonSegNet's superior speed and balanced accuracy on the Kvasir-SEG dataset.

Contribution

It provides a comprehensive benchmarking of state-of-the-art methods, introducing ColonSegNet with improved speed and accuracy for clinical application.

Findings

ColonSegNet achieved an average precision of 0.8000.

It attained a mean IoU of 0.8100.

It is the fastest with 180 frames per second for detection and localization.

Abstract

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.