School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.
School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
Comput Biol Med. 2024 Feb;169:107777. doi: 10.1016/j.compbiomed.2023.107777. Epub 2023 Dec 5.
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
医学图像识别是计算机辅助诊断、医学图像检索和挖掘中的一项重要任务。医学图像数据主要包括电子健康记录数据和基因信息数据等。尽管智能成像为医学图像分析提供了一个比传统方法更好的方案，传统方法依赖于手工制作的特征，但由于成像方式和临床病理的多样性，仍然具有挑战性。许多医学图像识别方法为医学图像分析提供了一个很好的方案。本文分析和总结了与方法相关的概念，例如机器学习、深度学习、卷积神经网络、迁移学习和其他图像处理技术在医学图像中的应用。我们回顾了这些最近的研究，提供了这些方法在各种医学图像分析任务中的综合概述，例如目标检测、图像分类、图像配准、分割等任务。特别是，我们强调了不同方法在医学图像分析中的最新进展和贡献，这些贡献是根据不同的应用场景总结的，包括分类、分割、检测和图像配准。此外，还总结了不同方法在不同应用领域的应用，例如肺部、脑部、数字病理学、脑部、皮肤、肺部、肾脏、乳房、神经多发性硬化、脊椎和肌肉骨骼等。最后总结了对开放挑战和未来研究方向的批判性讨论。特别是，未来将在计算机视觉、自然语言处理和无人驾驶等领域应用优秀的算法来进行医学图像识别。
