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Updated: Jan 16, 2026

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Colorectal Polyp Segmentation Based on Deep Learning Methods: A Systematic Review.

Xin Liu1, Nor Ashidi Mat Isa1, Chao Chen1,2

  • 1School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Pulau Pinang 14300, Malaysia.

Journal of Imaging
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

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This review systematically analyzes polyp segmentation methods, crucial for early colorectal cancer detection. It covers deep learning, Mamba, and video techniques, evaluating 44 models and datasets.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Colorectal cancer is a leading global malignancy.
  • Early polyp detection and assessment are vital for cancer prevention.
  • Polyp segmentation aids in targeted treatment planning.

Purpose of the Study:

  • To systematically review and analyze polyp segmentation methods.
  • To provide an overview of deep learning, Mamba, and video segmentation techniques.
  • To evaluate model performance and discuss future trends in polyp segmentation.

Main Methods:

  • Systematic literature review of 146 papers (2018-2024).
  • Analysis of deep learning, Mamba, and video-based polyp segmentation architectures.
  • Performance evaluation of 44 segmentation models using standard metrics.
Keywords:
Mambadeep learningpolyp segmentation

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Main Results:

  • Detailed analysis of the evolution of polyp segmentation techniques.
  • Comprehensive overview of current deep learning and Mamba-based methods.
  • Evaluation of segmentation performance and real-time capabilities of various models.

Conclusions:

  • Polyp segmentation is a rapidly evolving field with significant clinical implications.
  • Deep learning and emerging methods like Mamba show promise for improved accuracy.
  • Further research is needed to address current challenges and explore future trends.