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Multi-modal tumor segmentation methods based on deep learning: a narrative review.

Hengzhi Xue¹, Yudong Yao^2,3, Yueyang Teng^1,4

¹College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Quantitative Imaging in Medicine and Surgery

|January 15, 2024

Summary

This summary is machine-generated.

Deep learning (DL) methods significantly enhance multi-modal tumor segmentation accuracy by effectively fusing data from various imaging sources. This review explores recent DL advancements for improved clinical diagnosis and treatment planning.

Keywords:

Multi-modal image fusion methods review tumor segmentation

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Area of Science:

Medical Image Processing
Artificial Intelligence in Medicine
Radiology

Background:

Automatic tumor segmentation is vital for clinical diagnosis and treatment.
Multi-modal imaging offers a more comprehensive tumor understanding than single-modal approaches.
Deep learning (DL) methods have shown remarkable performance in medical image analysis.

Purpose of the Study:

To provide a comprehensive overview of recent deep learning-based multi-modal tumor segmentation methods.
To summarize key techniques and findings in the field.
To identify future research directions.

Main Methods:

Systematic literature search of PubMed and Google Scholar databases (January 2018 - June 2023).
Keywords used: "multi-modal", "deep learning", "tumor segmentation".
Review of 78 English articles.

Main Results:

Introduction of public datasets, evaluation metrics, and multi-modal data processing techniques.
Summary of common DL network architectures, strategies, and fusion methods for tumor segmentation.
Analysis of different tumor segmentation tasks.

Conclusions:

Deep learning is a powerful technique for multi-modal tumor segmentation.
Fusion methods enable DL frameworks to leverage diverse data characteristics, improving segmentation accuracy.
The study highlights the potential of DL for advancing multi-modal tumor segmentation.