BeFOI: A Novel Method Based on Conditional Diffusion Model for Medical Image Denoising

The progress in medical imaging technology highlights the importance of image quality for effective diagnosis and treatment.Yet,noise during capture and transmission can compromise image accuracy and reliability,complicating clinical decisions.The rising interest in diffusion models has led to their exploration of denoising images.We present Be-FOI(Better Fluoro Images),a weakly supervised model that uses cine images to denoise fluoroscopic images,both DR types.Trained through precise noise estimation and simulation,BeFOI employs Markov chains to denoise using only the fluoroscopic image as guidance.Our tests show that BeFOI outperforms other methods,reducing noise and enhancing clar-ity and diagnostic utility,making it an effective post-processing tool for medical images.

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

Improved Medical Image Segmentation Model Based on 3D U-Net

With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.

New Virtual Cutting Algorithms for 3D Surface Model Reconstructed from Medical Images

This paper proposes a practical algorithms of plane cutting, stereo clipping and arbitrary cutting for 3D surface model reconstructed from medical images. In plane cutting and stereo clipping algorithms, the 3D model is cut by plane or polyhedron. Lists of edge and vertex in every cut plane are established. From these lists the boundary contours are created and their relationship of embrace is ascertained. The region closed by the contours is triangulated using Delaunay triangulation algorithm. Arbitrary cutting operation creates cutting curve interactively. The cut model still maintains its correct topology structure. With these operations, tissues inside can be observed easily and it can aid doctors to diagnose. The methods can also be used in surgery planning of radiotherapy.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

A Review on Deep Learning in Medical Image Reconstruction

Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and treatment of diseases.Medical image reconstruction is one of the most fundamental and important components of medical imaging,whose major objective is to acquire high-quality medical images for clinical usage at the minimal cost and risk to the patients.Mathematical models in medical image reconstruction or,more generally,image restoration in computer vision have been playing a prominent role.Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed,and we shall call these models handcrafted models.Later,handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs,while part of the model is learned from the observed data.More recently,as more data and computation resources are made available,deep learning based models(or deep models)pushed the data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs.Both handcrafted and data-driven modeling have their own advantages and disadvantages.Typical handcrafted models are well interpretable with solid theoretical supports on the robustness,recoverability,complexity,etc.,whereas they may not be flexible and sophisticated enough to fully leverage large data sets.Data-driven models,especially deep models,on the other hand,are generally much more flexible and effective in extracting useful information from large data sets,while they are currently still in lack of theoretical foundations.Therefore,one of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches.The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint.This viewpoint stimulates new designs of neural network architectures with inspirations from optimization algorithms and numerical differential equations.Given the popularity of deep modeling,there are still vast remaining challenges in the field,as well as opportunities which we shall discuss at the end of this article.

Medical Image Segmentation Based on Wavelet Analysis and Gradient Vector Flow

Medical image segmentation is one of the key technologies in computer aided diagnosis. Due to the complexity and diversity of medical images, the wavelet multi-scale analysis is introduced into GVF (gradient vector flow) snake model. The modulus values of each scale and phase angle values are calculated using wavelet transform, and the local maximum points of modulus values, which are the contours of the object edges, are obtained along phase angle direction at each scale. Then, location of the edges of the object and segmentation is implemented by GVF snake model. The experiments on some medical images show that the improved algorithm has small amount of computation, fast convergence and good robustness to noise.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

The Ultra Wide Band Radar System Parameters in Medical Application

In this work, an Ultra Wide Band (UWB) radar system is proposed in an attempt to take a medical image of each hu-man body layer. In fact, this system consists of sending an electromagnetic pulse and analyzing the echo reflected by the human body tissue. In order to realize this system, the parameters which enable us to optimize the functionality of our radar are computed. Indeed, we fixed a frequency range, incident angle, pulse repetition frequency, the power and the antenna deployed by the UWB radar system in medicine. As well as, a human body model is presented in order to have practical results..

A CT Image Segmentation Algorithm Based on Level Set Method

Level Set methods are robust and efficient numerical tools for resolving curve evolution in image segmentation. This paper proposes a new image segmentation algorithm based on Mumford-Shah module. The method is used to CT images and the experiment results demonstrate its efficiency and veracity.

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020.The consequences of this virus are quite frightening,especially when accompanied by an underlying disease.The novelty of the virus,the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process.Although the new test kits provide almost certain results,chest X-rays are extremely important to detect the progression and degree of the disease.In addition to the Covid-19 virus,pneumonia and harmless opacity of the lungs also complicate the diagnosis.Considering the negative results caused by the virus and the treatment costs,the importance of fast and accurate diagnosis is clearly seen.In this context,deep learning methods appear as an extremely popular approach.In this study,a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease.In addition,in order to contribute to the literature,a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented.With this ensemble model design,quite remarkable results are obtained for the diagnosis of three and four-class Covid-19.The proposed model can classify normal,pneumonia,and Covid-19 with 92.6%accuracy and 82.6%for normal,pneumonia,Covid-19,and lung opacity.

Optimization of the UWB Radar System in Medical Imaging

During the last decades, we have witnessed a widespread deployment of the ultra wide band (UWB) radar systems. Considering a medical field, an algorithm optimizing these systems is pointed out in this contribution. Beginning with the description of the UWB radar system, this algorithm has proved to be not only able to take a medical image of the human body but also capable of diverting the human tissue. Moreover, we insist on the fact that this algorithm can easily optimize different radar parameters. So, the human body layer width, the incident angle and the frequency maximizing reflection coefficient are estimated in this paper.

医学影像学教学模式的现状与实践

医学影像学是临床医学中一门实践性极强辅助学科,同时也是发展迅猛,日新月异的学科,在现代医疗中扮演着越来越重要的角色。文章通过对影像医学教学的基本要求、教学目标和目的进行说明,采用影像存储和传输系统(picture archiving and communication system,PACS)结合以问题为基础的教学法(problem-based learning,PBL)及以案例为基础的教学法(case-based learning,CBL)教学法进行实践。针对非影像专业学员学习时间短、学习任务重,学员影像基础和需求迥异,部分非影像专业学员自身对医学影像学的重视不足以及放射基地重视不足等问题。文章阐述了PACS与PBL、CBL教学法、钉钉、微信及SPARK学习平台等学习沟通平台相结合,并增加专职人员对基地管理、带教老师一对一指导,以及不断加强科室建设、安排名师上课来提高科室影响力等多种混合教学模式的解决对策,来唤起学员学习影像相关知识的内驱力,达到提高学员轮转学习效果的目的。

翻转课堂在腹部影像学教学中的应用探讨

翻转课堂在腹部影像学教学中的应用为医学教育带来了革新。通过实践其核心思想,课前提供多元化的学习资源,有效激发了学生的主动学习热情,并帮助他们构建了个性化的学术发展路径。在腹部影像学教学中,翻转课堂强调实践操作与理论知识相结合,课堂时间更专注于实际病例分析、讨论与解决问题的实践环节。这种教学模式培养了学生的独立思考和团队协作能力,提升了实践技能水平。同时,在应用翻转课堂教学过程中,教师需关注学生学习主动性的培养、前期学习资源的设计与更新、技术设备与平台的支持以及学生群体差异等问题。通过克服这些挑战,教师能更好地帮助学生在腹部影像学领域取得重要的学术成果。翻转课堂的实践为腹部影像学教育开辟了新的教学路径,助力医学生更全面、深入地理解和应用影像学知识。

“互联网+”背景下BOPPPS教学模式在医学影像专业教学中的实践研究

目的探讨“互联网+”背景下BOPPPS教学模式在医学影像专业教学中的实践效果。方法选取2021年5月―2023年6月在大庆油田总医院放射科进行学习的80名实习生,根据不同的教学模式将其分为BOPPPS教学组和传统教学组,将采用传统教学模式的40名学生纳入传统教学组,将采用“互联网+”背景下BOPPPS教学模式的40名学生纳入BOPPPS教学组。比较2组实习生教学1个月后的教学效果评分和满意度。结果BOPPPS教学组的各项教学效果评分均高于传统教学组,差异有统计学意义(P<0.001)。BOPPPS教学组实习生的各项满意度和总满意度均高于传统教学组[(91.15±3.12)分vs.(84.25±5.58)分,(93.26±4.28)分vs.(81.64±5.19)分,(90.23±3.81)分vs.(80.47±5.31)分,(91.55±3.62)分vs.(82.12±5.31)分],差异有统计学意义(P<0.001)。结论在医学影像专业教学中采用“互联网+”背景下BOPPPS教学模式,可以提升医学影像专业的教学质量,提升实习生的教学满意度。

比较影像学在腹部影像诊断实习带教中的应用

随着医学成像技术的迅猛发展,各种影像学设备在临床实践中的应用日益广泛,对传统的医学影像学教学模式提出了新的要求。特别是腹部影像学所涉及器官多、病种多,不同疾病的影像学特征存在交叉、重叠,不同个体间同种疾病的影像学特征存在差异性。比较影像学作为一种新的教学模式,通过对照和比较的方法阐明各种成像技术对疾病成像的优势和缺点,对比不同疾病间影像学特征的相同点和差异性,从而有利于使医学生更系统全面地掌握腹部影像学的基础知识、诊断方法及鉴别诊断,了解不同成像诊断技术的优势与不足,更科学的合理选择、综合应用影像检查技术。文章分析了比较影像学在腹部影像实习带教中的必要性、可行性和实际价值,分享了在腹部影像诊断实习带教中应用比较影像学教学法的经验和思考,以期为提高教学效果、优化教学方法提供有益的参考。

基于OMP的“机旁教学”在放射科临床见习教学中的应用研究

目的:评价基于OMP的“机旁教学”在放射科临床见习教学中的应用效果。方法:选取117名全日制临床医学本科生,随机分为对照组和试验组。对照组采用传统授课模式,试验组采用基于OMP的“机旁教学”模式,见习结束后进行见习出科考试。结果:试验组的理论测试、影像报告书写和见习学习态度3项评分及总成绩均显著高于对照组(P<0.05)。结论:基于OMP的“机旁教学”在放射科临床见习教学中取得了较好的效果,有助于提升学生的学习效果和积极性。

基于BOPPPS教学模式下的虚拟现实技术在医学影像诊断教学中的运用研究

目的探讨基于BOPPPS教学模式下的虚拟现实技术在医学影像诊断教学中的运用及教学效果。方法60名临床医学专业实习学生被随机分为两组,每组30名。研究组采用基于BOPPPS教学模式下运用虚拟技术进行教学,对照组采用多媒体幻灯片的传统教学方法进行教学,比较两组学员的出科考核成绩、满意度等情况。结果研究组出科理论成绩、读片分析成绩、总成绩均高于对照组,差异均有统计学意义(t分别=7.50、16.14、16.55,P均<0.05)。研究组学生在巩固影像学基础知识、阅片思维能力提高、理论与临床同步、增强自我学习能力、对带教老师的满意度等方面的满意度及总满意度均高于对照组,差异均有统计学意义(t分别=14.64、11.40、11.53、11.16、10.52、29.45,P均<0.05)。结论基于BOPPPS教学模式下的虚拟现实技术可提高教学质量和教学效果,为医学影像教学提供了新的教学模式。

基于空间信息关注和纹理增强的短小染色体分类方法

染色体分类是核型分析中的重要任务之一。尽管残差神经网络已经在染色体分类领域取得了显著成就,但由于部分染色体具有长度较短、分类特征难以识别以及形态相似度较高的特点,使得其分类仍然具有挑战性。提出了基于空间信息关注和纹理增强的染色体分类模型(SIATE-Net),以Inception_ResNetV2模型作为骨干网络提取染色体的深层特征,自注意力机制和深度可分离卷积的引入能够更好地关注和保留短小染色体的空间信息。染色体长度较短易造成显带信息混淆,模型融入了纹理增强机制以扩大染色体间的差异性,为分类任务增加更多的判定依据。SIATE-Net模型分别在私人数据集与公开数据集上进行验证,分类性能明显优于其他方法,尤其是短小染色体。在私人数据集上,SIATE-Net模型表现出了最佳的总体分类准确率98.05%,短小染色体分类精度高达97.42%。在公开数据集上,SIATE-Net模型的总体分类准确率为98.95%,而短小染色体也达到了98.51%。实验结果表明,具有较强针对性的自注意力模块、深度可分离卷积和纹理增强模块在不降低整体分类准确性的前提下,能够有效地解决短小染色体分类任务。

基于机器学习的出血性脑卒中临床智能诊疗预测模型的建立

针对出血性脑卒中起病急、进展快且通常会导致脑组织机械性损伤和一系列复杂的生理病理反应等问题建立了一种基于机器学习的智能诊疗预测模型,使用人工智能技术对大量影像数据进行处理分析,随机抽取数据样本将模型应用于出血性脑卒中的临床诊疗预测中。与传统回归方法相比,机器学习方法在均方误差、平均绝对误差、平均绝对百分比误差上分别有62.08%、65.89%和47.33%的提升,证明机器学习智能诊疗预测模型可提高出血性脑卒中患者的预测准确率。