A review of deep learning approaches for multimodal image segmentation of liver cancer

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Applied Clinical Medical Physics Pub Date : 2024-10-07 DOI:10.1002/acm2.14540
Chaopeng Wu, Qiyao Chen, Haoyu Wang, Yu Guan, Zhangyang Mian, Cong Huang, Changli Ruan, Qibin Song, Hao Jiang, Jinghui Pan, Xiangpan Li
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Abstract

This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation for effective treatment and disease monitoring. Multimodal image fusion has the potential to offer more comprehensive information and more precise segmentation, and DL techniques have achieved remarkable progress in this domain. This paper starts with an introduction to liver cancer, then explains the preprocessing and fusion methods for multimodal images, then explores the application of DL methods in this area. Various DL architectures such as convolutional neural networks (CNN) and U-Net are discussed and their benefits in multimodal image fusion segmentation. Furthermore, various evaluation metrics and datasets currently used to measure the performance of segmentation models are reviewed. While reviewing the progress, the challenges of current research, such as data imbalance, model generalization, and model interpretability, are emphasized and future research directions are suggested. The application of DL in multimodal image segmentation for liver cancer is transforming the field of medical imaging and is expected to further enhance the accuracy and efficiency of clinical decision making. This review provides useful insights and guidance for medical practitioners.

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肝癌多模态图像分割的深度学习方法综述。
本综述探讨了应用于肝癌多模态融合图像分割的深度学习(DL)技术的最新发展。肝细胞癌是一种高度危险的恶性肿瘤,需要精确的图像分割才能进行有效的治疗和疾病监测。多模态图像融合有望提供更全面的信息和更精确的分割,DL 技术在这一领域取得了显著进展。本文首先介绍了肝癌,然后解释了多模态图像的预处理和融合方法,接着探讨了 DL 方法在这一领域的应用。本文讨论了卷积神经网络(CNN)和 U-Net 等各种 DL 架构及其在多模态图像融合分割中的优势。此外,还回顾了目前用于衡量分割模型性能的各种评估指标和数据集。在回顾研究进展的同时,强调了当前研究面临的挑战,如数据不平衡、模型泛化和模型可解释性,并提出了未来的研究方向。DL 在肝癌多模态图像分割中的应用正在改变医学成像领域,有望进一步提高临床决策的准确性和效率。本综述为医学从业者提供了有益的见解和指导。
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来源期刊
CiteScore
3.60
自引率
19.00%
发文量
331
审稿时长
3 months
期刊介绍: Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
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