影像设备的联网与DICOM标准遵从协议书

医学影像设备生产厂家提供的DICOM标准遵从协议声明书，在医院建立PACS系统，论证购买、验收医学影像设备的过程中是一个有用的工具，本文就本院对影像设备的论证、采购及实验性联网过程中使用该工具的认识和体会作一个介绍。     

医学图像存储与通信系统的技术与应用

医学图像存储与通信系统(PACS，Picture Archiving and Communication Systems)是临床医学、医学影像学、数字化影像技术与计算机技术、网络通讯技术结合的产物。此技术将医学影像资料转化为供计算机识别处理的数字形式，通过计算机及网络通讯设备，完成对医学影像信息采集、存储、处理及传输等功能，使医学影像信息资源共享。PACS技术是医院迈向数字化信息时代的重要标志之一，是医疗信息资源达到充分共享的关键，是从     

构建海量医学影像数据库的若干问题探讨

就构建海量医学影像数据库的信息存储技术、医学影像内容的检索、医学影像决策支持系统等方面进行探讨。     

医学影像的数字化采集与存储的研究

信息的数字化、网络化已成为人类经济活动和社会活动重要组成部分,而医学领域的医学影像数字化信息在医学诊断和教育中具有重要作用和特殊地位。本文在论述医学影像数字化的特点上,进而分析了其重要性和必要性,为医学影像信息的数字化全面实现提供理论基础。     

基于PACS的医学影像实践考试系统的开发及应用

目的探索网络考试系统在医学影像学现代化实践考试中的应用效果。方法开发基于医学影像教学PACS的网络实践考试系统,并通过试用于学生医学影像实践考试对其应用效果进行评估。结果所开发的医学影像网络实践考试系统将试题库与阅图工具、答题界面相结合,具有对考试题及考试人员的综合管理、调配及应用等功能。在学生医学影像实践考试的试用及满意度调查中,该考试系统获得平均得分96．75（满分100）,并得到较高的评价。结论该网络考试系统使实践考试的内容和信息更加丰富,形式自动化和科学化,应用于医学影像学的实践考试具有一定优势。     

PACS中的标准DICOM

医学影像存档与通讯系统PACS(Picture Archiving and Communication System)是医学影像学、数字化图像技术与计算技术，网络通讯技术结合的产物，是特殊的网络系统，是医院信息系统HIS(Hospital Information system)的重要组成部份。在新一代HIS的信息源中，来自于PACS的影像信息占到医院总信息量的80％以上。因此，对医学影像信息的格式、采集、存储，传送等标准化，是PACS系统中各种影像设备互连通讯需要解决的问题。     

医学影像存档与通讯系统在医疗流程重组中的作用

目的：探讨医学影像存档与通讯系统(PACS)在医疗流程重组中的作用。方法：结合医院实际情况，通过资料的复习，探讨在医院医疗流程重组过程中，医学影像存档与通讯系统(PACS)的应用。结果：PACS实施的技术关键在于医学影像数字化和医学数字影像通信标准化DC0M3．O；根据医院财力和技术能力，可阶段性实施PACS系统，以实现影像科室的诊断作业流程重组。结论：PACS系统的实施，改变了传统放射影像学的诊断模式，利于临床能迅速、准确获得所需要的医学影像信息及其相关的医学影像诊断报告、临床资料等，便于综合分析、作出明确诊断和拟定恰当的治疗方案。在提高医疗服务质量的前提下，是医院实行医疗流程重组、实现资源成本最小化和提高竞争能力的有效途径。     

医学影像读取显示的研究与实践

本文简述了目前常用的医学影像格式，在Microsoft Visual C＋＋6．0环境下结合中国科学院自动化研究所开发的MITK（Medical Imaging Toolkit）工具，对医学影像的读取与显示进行了研究，并实现了一个小型医学影像读取及显示的软件。     

关于医学影像数字化方法的探讨

本文分析了医学影像数字化的重要意义,详细探讨了不同类型的医学影像实现数字化的具体方法和途径,为全面实现医学影像信息的数字化提供了思路.     

医学影像资料的数字化管理

归纳总结各种数字化影像资料管理方法包括数码拍摄、数码扫描、磁盘存储及刻录光盘等数字存储，直接的医学影像存档与传输系统（PACS），并对比分析各种方法的先进性。数字化医学影像管理方便快捷，具有节省空间、安全、传输方便等优点，已成为医学影像资料管理的发展趋势，PACS系统的广泛应用及网络化的进展将使数字化医学影像资料管理更加科学化、信息化。     

Medical Image Analysis by Cognitive Information Systems – a Review

This publication presents a review of medical image analysis systems. The paradigms of cognitive information systems will be presented by examples of medical image analysis systems. The semantic processes present as it is applied to different types of medical images. Cognitive information systems were defined on the basis of methods for the semantic analysis and interpretation of information – medical images – applied to cognitive meaning of medical images contained in analyzed data sets. Semantic analysis was proposed to analyzed the meaning of data. Meaning is included in information, for example in medical images. Medical image analysis will be presented and discussed as they are applied to various types of medical images, presented selected human organs, with different pathologies. Those images were analyzed using different classes of cognitive information systems. Cognitive information systems dedicated to medical image analysis was also defined for the decision supporting tasks. This process is very important for example in diagnostic and therapy processes, in the selection of semantic aspects/features, from analyzed data sets. Those features allow to create a new way of analysis.     

区域医疗影像信息共享的实现

本文阐述了我院影像存储与通讯系统（PACS）与福建省居民健康档案管理中心影像库对接以实现区域医疗影像信息共享的全过程,并介绍了病人检查信息与检查影像资料的上传及区域内其他医院相关资料的调用方法。区域医疗影像信息共享的实现,可推动区域医疗卫生信息化建设,提升医疗服务再利用率,降低区域整体医疗费用。     

基于多维度特征融合的深度学习骨龄评估模型

目的 通过基于特征提取的深度卷积神经网络,结合关键区域特征和人口学信息,评估儿童骨龄。方法 自动识别左手X线图像数据,对图像进行预处理,使用基于深度神经网络的X线图像分析方法,实现左手关节骨龄17个关键区域特征的自动提取,再将骨龄影像特征与临床大数据（人口统计、性别）融合训练骨龄评估模型,测试模型的评估效能。结果 使用基于深度学习的特征区域提取方法比传统图像分析方法可以更好地提取特征信息,结合临床信息从另一维度补充了骨龄发育信息。基于多维度数据特征融合的骨龄评估模型检测得到的骨龄平均绝对误差为0.455,优于传统方法和仅端到端的深度学习方法。结论 相较传统的机器学习特征提取方法,基于特征提取的深度卷积神经网络在骨龄回归模型上有更好的表现,结合人口和性别信息可进一步提升基于图像的骨龄预测准确率。     

Medical Image Analysis using Convolutional Neural Networks: A Review

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.     

人工智能在医学影像中的应用

阐述“人工智能+医学影像”的含义,介绍人工智能技术在疾病筛查、辅助治疗、病理分析中的主要应用,指出现阶段人工智能在医学影像应用中面临的主要问题,展望人工智能助力智能医疗的发展前景。     

Energy Spectrum CT Image Detection Based Dimensionality Reduction with Phase Congruency

The image feature detection is widely used in image registration, image stitching and object recognition. The feature detection algorithm can be applied to the detection of artificial images, and can be used to detect the energy spectrum CT image. A new algorithm of phase consistency detection based on dimensionality reduction is proposed in this paper. We mainly focus on the phase congruency of the spectral CT images in the paper and try to use dimensionality reduction to integrate the information of phase congruency detected in the image. The experimental results show that the algorithm can detect the energy spectrum CT image with clear edge and contour, which is beneficial to the subsequent processing. Meanwhile, the algorithm presented is more effective in diagnosis of disease for medical professionals.     

基于深度学习的人工智能技术在腮腺肿瘤诊治的实践及思考

基于深度学习的人工智能技术已被广泛应用于计算机视觉领域,在医学图像处理方面,基于卷积的深度学习神经网络具备较好的智能学习和目标区域关键信息分析处理能力,在各类医学影像的图像分割实践中表现出近似于甚至高于专业人员的智能水平。腮腺是唾液腺肿瘤好发的腺体,腮腺肿瘤是口腔颌面外科的常见病和多发病,对腮腺肿瘤的精准诊疗仍存在临床挑战。本研究围绕深度学习技术在腮腺肿瘤智能诊疗的应用和前景作一述评,希冀推动口腔智慧医疗的进一步深化及发展。     

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.     

Research and applications: Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library

Background

Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging.

Objective

To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort.

Methods

As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images.

Results

Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%.

Conclusions

The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.     

原发性骨髓纤维化转化为急性巨核细胞白血病1例及文献复习

目的报道1例由原发性骨髓纤维化转化的急性巨核细胞白血病（M7）,并讨论转化前后骨髓MRI表现的改变。方法对我院1例由原发性骨髓纤维化转化为急性巨核细胞白血病的患者在转变前后进行骨髓细胞形态学、细胞遗传学检测,并同步进行髂骨骨髓的MRI检查;使用标准IDA、HA、MA、ACLA等方案序贯治疗,并随访至今。结果在患者处于原发性骨髓纤维化阶段时,髂骨骨髓在MRI上表现为T1WI及T2WI上的片状低信号;而在转化后相同部位骨髓在MRI上表现为T1WI片状低信号,T2WI上散在高信号。该患者（染色体检测提示核型正常）经序贯化疗后达到完全缓解至今。结论原发性骨髓纤维化与急性巨核细胞性白血病在MRI上骨髓表现有明显的改变;核型正常的由骨髓纤维化转变的M7经合理序贯化疗后仍能达到完全缓解。