Deep learning for MRI lesion segmentation in rectal cancer.

Area of Science:

• Oncology
• Radiology
• Artificial Intelligence

Background:

• Rectal cancer (RC) poses global health challenges, with Magnetic Resonance Imaging (MRI) being a key diagnostic tool.
• Current diagnostic accuracy in RC relies on radiologist expertise, facing limitations like fatigue and image clarity issues.
• Complex anatomical structures and similar organ shapes in MRI hinder precise RC diagnosis.

Purpose of the Study:

• To review the development of deep learning (DL) segmentation algorithms for rectal cancer.
• To discuss the application progress of DL in segmenting rectal cancer lesions from MRI images.
• To provide theoretical guidance for advancing DL in rectal cancer diagnostics.

Main Methods:

• Review of deep learning segmentation techniques applied to medical imaging.
• Analysis of current research on DL for rectal cancer lesion segmentation in MRI.
• Synthesis of findings to assess the potential of DL in improving diagnostic accuracy.

Main Results:

• Deep learning shows significant potential in medical image analysis, particularly in classification, detection, and segmentation.
• DL algorithms are increasingly applied to enhance the accuracy of lesion segmentation in rectal cancer MRI.
• Advancements in DL offer improved capabilities for identifying and delineating rectal cancer.

Conclusions:

• Deep learning segmentation holds promise for overcoming limitations in manual rectal cancer diagnosis from MRI.
• Further development and application of DL algorithms can significantly improve the accuracy and efficiency of rectal cancer detection.
• This review provides a foundation for future research in AI-driven rectal cancer diagnostics.