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

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.

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Cited by in corpus (16)

• Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers
• Efficient Medical Image Segmentation Based on Knowledge Distillation
• HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS
• FCN-Transformer Feature Fusion for Polyp Segmentation
• Video Polyp Segmentation: A Deep Learning Perspective
• A multi-centre polyp detection and segmentation dataset for generalisability assessment
• SinGAN-Seg: Synthetic training data generation for medical image segmentation
• A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
• Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
• Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations
• PVTAdpNet: Polyp Segmentation using Pyramid vision transformer with a novel Adapter block
• DivergentNets: Medical Image Segmentation by Network Ensemble
• Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models
• Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
• Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network