Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis

AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.

Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Computer-aided diagnosis(CAD) has become one of the major research subjects in medical imaging and diagnostic radiology.The basic concept of CAD is to provide computer output as a second opinion to assist radiologists' image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time.To date,research on CAD in ultrasound(US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology,with most studies being conducted using B-mode US images.Two CAD schemes with contrast-enhanced US(CEUS) that are used in classifying focal liver lesions(FLLs) as liver metastasis,hemangioma,or three histologically differentiated types of hepatocellular carcinoma(HCC) are introduced in this article:one is based on physicians' subjective pattern classifications(subjective analysis) and the other is a computerized scheme for classification of FLLs(quantitative analysis).Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis,93.3% and 93.8% for hemangioma,and 98.6% and 86.9% for all HCCs,respectively.In addition,the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs,41.7% and 50.0% for moderately differentiated HCCs,and 80.0% and 77.8% for poorly differentiated HCCs,respectively.There are a number of issues concerning the clinical application of CAD for CEUS,however,it is likely that CAD for CEUS of the liver will make great progress in the future.

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.

Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

Alzheimer’s disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (ANN) and a support vector machine (SVM), which were trained with 4 morphological and 6 functional image features, to 15 AD patients and 15 clinically normal (CN) subjects for classification of AD. The area under the receiver operating characteristic curve (AUC) values for the two systems based on the ANN and SVM with both image?features were 0.901 and 0.915, respectively. The AUC values for the ANN-and SVM-based systems with the morphological features were 0.710 and 0.660, respectively, and those with the functional features were 0.878 and 0.903, respectively. Our preliminary results suggest that the proposed method may have potential for assisting radiologists in the differential diagnosis of AD patients by using morphological and functional image features.

Complications of CAD/CAM Fabricated Surgical Template for Static Computer-Aided Implant Surgery

Purpose: Surgical templates produced by digital simulation and CAD/CAM allow for three-dimensional control of implant placement. However, due to clinical limitations, there are complications during the use of the template. The purpose of this study was to summarize the complications associated with the use of surgical templates for static computer-aided implant surgery. Methods: Complications were collected during the observation period, and then their implant sites were reanalyzed with simulation software. Results: There were 104 cases during the observation period, 5 cases had complications. Mechanical complications were observed in four cases, including three cases in which the frame of the template fractured during implant placement surgery and one case in which the sleeve fell off the surgical template. In one case, there was an error in the planned position. All cases were mandibular molar cases, and all cases of frame fracture were at the free end defect site. All cases had a Hounsfield unit of more than 700 at the implant site, and some of them had a significantly small jaw opening. Conclusion: Although the spread of CAD/CAM surgical templates has made it possible to avoid problems caused by the position of the implant, it has been difficult to avoid fractures in cases of mandibular free end defects with high Hounsfield unit.

基于CATIA的钢结构CAD与CAE一体化实现方法

为了应对钢结构在实现计算机辅助设计(CAD)与计算机辅助工程(CAE)一体化时的特殊性所带来的挑战,分析和归纳CAD与CAE一体化中数据转换,与几何理想化等关键问题,在此基础上,提出一种基于CATIA V6软件实现钢结构CAD与CAE一体化的方法,并通过一个绑扎桥钢结构的设计案例验证了该方法的有效性。通过所提方法分别实现了CAE几何理想化的自动切换与整合载荷数据至CAD模型的技术目标,从而避免了基于传统方法所带来的数据分离问题,减少了人工介入的低效工作。

Computer Aided Design Technology for Convex Faceted Gem Cuts Based on the Half-Edge Data Structure

Aiming to increase the efficiency of gem design and manufacturing, a new method in computer-aided-design (CAD) of convex faceted gem cuts (CFGC) based on Half-edge data structure (HDS), including the algorithms for the implementation is presented in this work. By using object-oriented methods, geometrical elements of CFGC are classified and responding geometrical feature classes are established. Each class is implemented and embedded based on the gem process. Matrix arithmetic and analytical geometry are used to derive the affine transformation and the cutting algorithm. Based on the demand for a diversity of gem cuts, CAD functions both for free-style faceted cuts and parametric designs of typical cuts and visualization and human-computer interactions of the CAD system including two-dimensional and three-dimensional interactions have been realized which enhances the flexibility and universality of the CAD system. Furthermore, data in this CAD system can also be used directly by the gem CAM module, which will promote the gem CAD/CAM integration.

SonoVCADS在正常胎儿心脏诊断切面显示中的可行性分析

目的探讨基于容积超声图像的心脏计算机辅助系统(SonoVCADS)在正常胎儿心脏诊断切面显示中的可行性。方法选取2021年8月至2022年3月在丽水市中心医院超声科行胎儿心脏检查的66名正常单胎胎儿为研究对象,使用SonoVCADS获得容积超声图像314个。由3名不同经验的观察者对每个诊断切面进行评分,计算SonoVCADS显示诊断切面的成功率和一致性,比较3名观察者使用SonoVCADS显示胎儿心脏诊断切面的时间。另随机选取50名胎儿,比较SonoVCADS与二维超声心动图显示胎儿心脏诊断切面的时间。结果3名观察者使用SonoVCADS显示胎儿心脏8个诊断切面的成功率比较,差异均无统计学意义(均P>0.05)。以观察者B(相对经验不足)为例,70.70%的容积超声图像可以成功显示8个诊断切面,13.69%的容积超声图像可以成功显示7个诊断切面,仅4.15%的容积超声图像成功显示的诊断切面数<4个。观察者B的8个诊断切面的Kappa值为0.652~0.854;3名观察者的8个诊断切面的Kappa值为0.536~0.752。观察者C(检查经验丰富)花费的时间明显少于观察者A和观察者B(均P<0.05)。观察者C使用SonoVCADS显示胎儿心脏8个诊断切面的时间明显短于二维超声心动图(P=0.018)。结论SonoVCADS显示胎儿心脏8个诊断切面的成功率较高,对操作人员的依赖性低,且具有良好的一致性和检查效率。

A Computer-aided Design System for Framed-mould in Autoclave Processing

The general computer-aided design （CAD） software cannot meet the mould design requirement of the autoclave process for composites, because many parameters such as temperature and pressure should be considered in the mould design process, in addition to the material and geometry of the part. A framed-mould computer-aided design system （FMCAD） used in the autoclave moulding process is proposed in this paper. A function model of the software is presented, in which influence factors such as part structure, mould structure, and process parameters are considered; a design model of the software is established using object oriented （O-O） technology to integrate the stiffness calculation, temperature field calculation, and deformation field calculation of mould in the design, and in the design model, a hybrid model of mould based on calculation feature and form feature is presented to support those calculations. A prototype system is developed, in which a mould design process wizard is built to integrate the input information, calculation, analysis, data storage, display, and design results of mould design. Finally, three design examples are used to verify the prototype.

工程学科常用CAD软件及功能分析

计算机辅助设计(Computer Aided Design,CAD)已经成为机械、电子、航天、化工及建筑等产业中不可或缺的工具之一。概述CAD软件行业的最新发展趋势和我国在该领域的进展,分析CAD软件的技术创新、产业布局以及国内企业在底层技术研发方面的进展。通过调研国际知名CAD软件公司的最新产品和策略以及国内企业的研发成果,分析CAD软件行业的技术动向。结果显示,CAD软件正朝着云端化、集成化、一体化以及智能化的方向发展,国内外企业都在不断探索新技术与CAD软件的结合,以提高设计效率和产品质量。我国CAD软件行业在政策支持和市场需求增长下将迎来更大发展空间,应加强自主研发和创新力度,以推出更多具有自主知识产权和核心竞争力的CAD产品。

Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.

机械零部件的测绘和CAD制图技术

随着现代工业的不断发展,机械零部件的测绘及计算机辅助设计(Computer Aided Design,CAD)制图研究成为工程设计中不可或缺的一环。以机械零部件测绘概述为基础,分析机械零部件测绘流程,详细讨论CAD制图技术在机械零部件测绘中的应用。经过实践证明,机械零部件测绘准确性对于工程设计的成功至关重要,而CAD制图技术应用则能大幅度提高测绘效率。

Computer-Aided Design of X-Ray Microtomographic Scanners

The article is to study the development of computer-aided design of X-ray microtomography—the device for investigating the structure and construction of three-dimensional images of organic and inorganic objects on the basis of shadow projections. This article provides basic information regarding CAD of X-ray microtomography and a scheme consisting of three levels. The article also shows basic relations of X-ray computed tomography, the generalized scheme of an X-ray microtomographic scanner. The methods of X-ray imaging of the spatial microstructure and morphometry of materials are described. The main characteristics of an X-ray microtomographic scanner, the X-ray source, X-ray optical elements and mechanical components of the positioning system are shown. The block scheme and software functional scheme for intelligent neural network system of analysis of the internal microstructure of objects are presented. The method of choice of design parameters of CAD of X-ray microtomography aims at improving the quality of design and reducing costs of it. It is supposed to reduce the design time and eliminate the growing number of engineers involved in development and construction of X-ray microtomographic scanners.

Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans

BACKGROUND Pulmonary tuberculosis(TB)and lung cancer(LC)are common diseases with a high incidence and similar symptoms,which may be misdiagnosed by radiologists,thus delaying the best treatment opportunity for patients.AIM To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography(CT)images.METHODS We enrolled 478 patients(January 2012 to October 2018),who underwent preoperative CT screening.Radiomics features were extracted and selected from the CT data to establish a logistic regression model.A radiomics nomogram model was constructed,with the receiver operating characteristic,decision and calibration curves plotted to evaluate the discriminative performance.RESULTS Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance.The radiomics nomogram model exhibited good discrimination,with an area under the curve of 0.914(sensitivity=0.890,specificity=0.796)in the training cohort,and 0.900(sensitivity=0.788,specificity=0.907)in the validation cohort.The decision curve analysis revealed that the constructed nomogram had clinical usefulness.CONCLUSION These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.

Image features related to margin and enhancement pattern improve the performance of computer-aided diagnosis for hepatic diseases using multi-phase computed tomography

Background This study aimed to develop features related to the lesion margin and enhancement pattern, which are very important in the radiologic diagnostic process. We also aimed to implement and investigate these features in the computer- aided diagnosis （CAD） of hepatic diseases using computed tomography （CT）. Methods We retrospectively analyzed 378 lesions with 1 512 multi-phase CT images of tiver lesions. We used ensemble methods to create classification models. Two types of features were developed and used as predictors, namely, margin features and relative spatial intensity ratio （RSIR） features. Margin features were extracted using Gabor transformation and the sigmoid function whereas RSIR features were obtained by calculating the concentration and distribution of the contrast in the lesion against the surrounding hepatic parenchyma. To assess these two types of features and compare them with other features used in previous studies, we created models for multi-class classification using different feature subsets. Accuracy, kappa, and AUC were calculated. The importance and interactions of predictors were also estimated. Results The classification model with margin features exhibited the best performance （accuracy： 0.89±0.04; kappa： 0.85±0.06）, followed by that with RISR features （accuracy： 0.85±0.05; kappa： 0.79±0.07）. The plots for variable importance and interactions also showed these two types of features were important in classification models and that they interacted with other features. Conclusions Lesion margin and enhancement pattern are helpful in CAD. The features we have developed are general and can be easily adapted to other diagnostic scenarios in which CT and other imaging modalities are used.

PWM VLSI Neural Network for Fault Diagnosis

An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.

A Method of Using Information Entropy of an Image as an Effective Feature for Com-puter-Aided Diagnostic Applications

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. In general, a CAD system employs a classifier to detect or distinguish between abnormal and normal tissues on images. In the phase of classification, a set of image features and/or texture features extracted from the images are commonly used. In this article, we investigated the characteristic of the output entropy of an image and demonstrated the usefulness of the output entropy acting as a texture feature in CAD systems. In order to validate the effectiveness and superiority of the output-entropy-based texture feature, two well-known texture features, i.e., mean and standard deviation were used for comparison. The database used in this study comprised 50 CT images obtained from 10 patients with pulmonary nodules, and 50 CT images obtained from 5 normal subjects. We used a support vector machine for classification. A leave-one-out method was employed for training and classification. Three combinations of texture features, i.e., mean and entropy, standard deviation and entropy, and standard deviation and mean were used as the inputs to the classifier. Three different regions of interest (ROI) sizes, i.e., 11 × 11, 9 × 9 and 7 × 7 pixels from the database were selected for computation of the feature values. Our experimental results show that the combination of entropy and standard deviation is significantly better than both the combination of mean and entropy and that of standard deviation and mean in the case of the ROI size of 11 × 11 pixels (p < 0.05). These results suggest that information entropy of an image can be used as an effective feature for CAD applications.

CAD技术在现代机械制造中的应用与发展

在现代机械制造的背景下,介绍计算机辅助设计(ComputerAided Design,CAD)技术的基本内容和发展历程,探讨CAD技术在机械制造的结构设计、工艺规划、3D打印等方面的应用及发展,同时总结CAD技术在机械制造中的应用优势和面临的挑战,展望其未来的发展方向。

A robust system for melanoma diagnosis using heterogeneous image databases

Early diagnosis of melanoma is essential for the fight against this skin cancer. Many melanoma detection systems have been developed in recent years. The growth of interest in telemedicine pushes for the development of offsite CADs. These tools might be used by general physicians and dermatologists as a second advice on submission of skin lesion slides via internet. They also can be used for indexation in medical content image base retrieval. A key issue inherent to these CADs is non-heterogeneity of databases obtained with different apparatuses and acquisition techniques and conditions. We hereafter address the problem of training database heterogeneity by developing a robust methodology for analysis and decision that deals with this problem by accurate choice of features according to the relevance of their discriminative attributes for neural network classification. The digitized lesion image is first of all segmented using a hybrid approach based on morphological treatments and active contours. Then, clinical descriptions of malignancy signs are quantified in a set of features that summarize the geometric and photometric features of the lesion. Sequential forward selection (SFS) method is applied to this set to select the most relevant features. A general regression network (GRNN) is then used for the classification of lesions. We tested this approach with color skin lesion images from digitized slides data base selected by expert dermatologists from the hospital “CHU de Rouen-France” and from the hospital “CHU H&#233;di Chaker de Sfax-Tunisia”. The performance of the system is assessed using the index area (Az) of the ROC curve (Receiver Operating Characteristic curve). The classification permitted to have an Az score of 89,10%.