TY - JOUR
T1 - Multi-modal tumor segmentation methods based on deep learning
T2 - a narrative review
AU - Xue, Hengzhi
AU - Yao, Yudong
AU - Teng, Yueyang
N1 - Publisher Copyright:
© 2024 AME Publishing Company. All rights reserved.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - fusion methods
KW - Multi-modal image
KW - review
KW - tumor segmentation
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U2 - 10.21037/qims-23-818
DO - 10.21037/qims-23-818
M3 - Review article
AN - SCOPUS:85181915695
SN - 2223-4292
VL - 14
SP - 1122
EP - 1140
JO - Quantitative Imaging in Medicine and Surgery
JF - Quantitative Imaging in Medicine and Surgery
IS - 1
ER -