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

• Published in: Journal of applied clinical medical physics Vol. 25; no. 12; pp. e14540 - n/a
• Main Authors: Wu, Chaopeng, Chen, Qiyao, Wang, Haoyu, Guan, Yu, Mian, Zhangyang, Huang, Cong, Ruan, Changli, Song, Qibin, Jiang, Hao, Pan, Jinghui, Li, Xiangpan
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.12.2024; John Wiley and Sons Inc
• Subjects: Abdomen; Accuracy; Algorithms; Artificial intelligence; Asymptomatic; Cancer therapies; Carcinoma, Hepatocellular - diagnostic imaging; Cholangiocarcinoma; Contrast agents; Deep Learning; Humans; Image Processing, Computer-Assisted - methods; Liver cancer; Liver diseases; Liver Neoplasms - diagnostic imaging; Medical prognosis; Metabolism; Mortality; multimodal fusion technology; Multimodal Imaging - methods; Neural networks; Neural Networks, Computer; Radiation therapy; Review; segmentation; Semantics; Tomography; Tumors; China; deep learning; segmentation; liver cancer; multimodal fusion technology
• ISSN: 1526-9914; 1526-9914
• DOI: 10.1002/acm2.14540

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.