A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends

Abstract

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more outstanding medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in this field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, then detail benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp sizes, considering the pain points of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in this field. The models, benchmark datasets, and source code links we collected are all published at https://github.com/taozh2017/Awesome-Polyp-Segmentation.

Have been published in Visual Intelligence

References in corpus (21)

• PVT v2: Improved Baselines with Pyramid Vision Transformer
• Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
• UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation
• 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
• PraNet: Parallel Reverse Attention Network for Polyp Segmentation
• Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
• BDG-Net: Boundary Distribution Guided Network for Accurate Polyp Segmentation
• Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
• TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing
• Automatic Polyp Segmentation via Multi-scale Subtraction Network
• Context-aware Cross-level Fusion Network for Camouflaged Object Detection
• DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
• Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation
• DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation
• S${}^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
• Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
• Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation

Cited by in corpus (2)

• Deep Bayesian segmentation for colon polyps: Well-calibrated predictions in medical imaging
• Frontiers in Intelligent Colonoscopy