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

Hengzhi Xue, Yudong Yao, Yueyang Teng

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Background and Objective: Automatic tumor segmentation is a critical component in clinical diagnosis and treatment. Although single-modal imaging provides useful information, multi-modal imaging provides a more comprehensive understanding of the tumor. Multi-modal tumor segmentation has been an essential topic in medical image processing. With the remarkable performance of deep learning (DL) methods in medical image analysis, multi-modal tumor segmentation based on DL has attracted significant attention. This study aimed to provide an overview of recent DL-based multi-modal tumor segmentation methods. Methods: In in the PubMed and Google Scholar databases, the keywords "multi-modal", "deep learning", and "tumor segmentation" were used to systematically search English articles in the past 5 years. The date range was from 1 January 2018 to 1 June 2023. A total of 78 English articles were reviewed. Key Content and Findings: We introduce public datasets, evaluation methods, and multi-modal data processing. We also summarize common DL network structures, techniques, and multi-modal image fusion methods used in different tumor segmentation tasks. Finally, we conclude this study by presenting perspectives for future research. Conclusions: In multi-modal tumor segmentation tasks, DL technique is a powerful method. With the fusion methods of different modal data, the DL framework can effectively use the characteristics of different modal data to improve the accuracy of tumor segmentation.

Original languageEnglish
Pages (from-to)1122-1140
Number of pages19
JournalQuantitative Imaging in Medicine and Surgery
Volume14
Issue number1
DOIs
StatePublished - Jan 2024

Keywords

  • fusion methods
  • Multi-modal image
  • review
  • tumor segmentation

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