Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.
影像融合目前在前列腺癌的诊断中发挥着重要作用，选择和开发出良好的影像融合算法是实现影像融合的核心任务，决定了所得到的融合图像质量是否优良，是否能满足临床应用的实际需求，因此成为医学图像融合的研究热点之一。为了对医学影像融合方法进行全面研究，本文对近年来国内外发表的相关文献进行综述。对影像融合技术进行分类，将影像融合算法分为传统融合算法和深度学习融合算法，对一些算法的原理和工作流程进行深入分析对比，总结其优缺点，并介绍了相关的医学影像数据集。最后，本文对医学影像融合算法的未来发展趋势进行了展望，为医学影像融合技术辅助诊断前列腺癌等重大疾病指明了发展方向。.
Keywords: Deep learning; Image fusion; Prostate cancer; Traditional machine learning.