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深度学习在医学影像智能处理中的应用与挑战

左艳,黄钢,聂生东(上海理工大学医疗器械与食品学院, 上海 200093;上海健康医学院医学影像学院, 上海 201318)

摘 要
利用深度学习方法对医学影像数据进行处理分析,极大地促进了精准医疗和个性化医疗的快速发展。深度学习在医学图像领域的应用较为广泛,具有多病种、多模态、多组学和多功能的特点。为便于对深度学习在医学图像处理领域的应用进行更深入有效的探索,本文系统综述了相关研究进展。首先,从深度学习在影像基因组学中的应用出发,理清了深度学习在医学影像领域应用的一般思路和现状,将医学影像领域分为智能诊断、疗效评估和预测预后等3个模块,并对模块内的各病种进行总结,展示了深度学习各算法的优缺点及面临的问题和挑战。其次,对深度学习中出现的新思路、新方法以及对传统方法的改进进行了阐述。最后,总结了该领域现阶段面临的问题,并对未来的研究方向做出了展望。基于深度学习的医学图像智能处理与分析虽然取得了一些有价值的研究成果,但还需要根据临床的实际需求,将深度学习与经典的机器学习算法及无创并且高效的多组学数据结合起来,对深度学习的理论和方法进行深入研究。
关键词
Application and challenges of deep learning in the intelligent processing of medical images

Zuo Yan,Huang Gang,Nie Shengdong(School of Medical Instrument and Fool Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China)

Abstract
The amount of medical imaging data is increasing rapidly every year. Although large-scale medical imaging data pose considerable challenges to the work of clinicians, they also offer opportunities for improving disease diagnosis and treatment models. Algorithms based on deep learning exhibit advantages over humans in processing big data, analyzing complex and nondeterministic data, and delving into potential information that can be obtained from data. In recent years, an increasing number of scholars have use deep learning to process and analyze medical image data, promoting the rapid development of precision medicine and personalized medicine. The application of deep learning to medical image processing and analysis, which are characterized by multiple diseases, modals, functions, and omics, is relatively extensive. To facilitate the further exploration and effective application of deep learning methods by researchers in the field of medical image processing, this study systematically reviewed relevant research progress, expecting that such review will be beneficial for researchers in this field. First, general thoughts and the current situation of the application of deep learning to medical imaging were clarified from the perspective of deep learning applications to imaging genomics. Second, state-of-the-art ideas and methods and recent improvements in original deep learning methods were comprehensively described. Lastly, existing problems in this field were highlighted and development trends were explored. In accordance with application status, the application of deep learning to medical imaging was divided into three modules: intelligent diagnosis, response evaluation, and prediction prognosis. The modules were subdivided into different diseases for summary, and the advantages and disadvantages of each deep learning method and existing problems and challenges were highlighted. In terms of intelligent diagnosis, the disadvantages of manual doctor diagnosis, such as heavy workload, subjective cognitive susceptibility, low efficiency, and high misdiagnosis rate, are becoming increasingly evident due to the increasing complexity of medical imaging information. The use of deep learning to interpret medical images and then comparing the results with other case records will help doctors locate lesions and assist in diagnosis. Moreover, the burden of doctors and medical misjudgments can be effectively reduced, improving the accuracy of diagnosis and treatment. Further research on the applications of deep learning and computer vision technologies to radiography is a pressing task in the 21st century, particularly for diseases with high incidence, such as brain and fundus disorders. In the follow-up study, we should focus on optimizing the generation of labels, specifying precise pathological regions in medical images, and establishing a strong supervision model instead of a weak one. In addition, deploying a cropping algorithm on a picture archiving and communication system platform will pave the way to algorithm improvement and entry to the clinical environment. In terms of response evaluation, the pathological evaluation of surgical specimens is the only reliable indicator of long-term tumor prognosis. However, these pathological data can only be obtained after completing all preoperative and surgical treatments, and they cannot be used as a guide for adjusting treatment. The development of noninvasive biomarkers with early prediction potential is important. At present, most relevant studies have conducted analysis by using traditional machine learning algorithms or statistical methods. Biological and clinical data extracted using medical imaging artificial intelligence programs designed by precision medicine researchers can determine the level of lymphocyte infiltration into tumors, predict imaging omics indicators of the therapeutic effect of immunotherapy to patients, and guide chemoradiotherapy treatment. The realization and development of this technique are of considerable clinical significance and deserve additional effort from researchers. With regard to prediction prognosis, imaging markers can predict the mutation status of genes, the molecular categories that regulate the activity of treatment-related proteins, and disease status and prognosis by using deep learning. Intelligent processing and analysis of medical images using deep learning is noninvasive, repeatable, and inexpensive. In the succeeding research, the data fusion of different omics should be completed to realize a link model of the reasoning mechanism based on content and semantics. Moreover, a fast retrieval method for structured data should be established by using the correlation relationship among data to develop an intelligent prediction model with high accuracy and strong robustness. Valuable research results and meaningful progress of the intelligent processing and analysis of medical images based on deep learning have been obtained; however, they have not been widely used in the clinical setting. In-depth research on deep learning theories and methods should be conducted further. In particular, the acquisition of a large number of high-quality labeled imaging cases, multicenter research and verification, the visualization of the decision-making process and diagnosis basis, and the establishment of a tripartite evaluation system are critical. Moreover, the development of intelligent medical imaging requires the fusion of big data and medical imaging technologies, clinical experience and multiomics big data, and artificial intelligence and medical imaging capabilities. Medical problems and clinical results should be used as guides to realize micro/macro system precision micro-closed-loop research for solving practical clinical problems, such as accurate tumor segmentation before, during, and after surgery; intelligent disease diagnosis; and noninvasive tracking of treatment effect, treatment response, and disease status.
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