人体CT切片图像中骨骼的分割

在实现人体骨骼的三维可视化中 ,首要的一步是将骨骼从二维图像中分割出来。本研究选用美国国家医学图书馆提供的可视人体项目中 1733张女性 CT切片数据 ,提出了一套图像去噪、分割和平滑的处理方法。在去噪中使用了 Chebyshev一致逼近滤波技术 ;在分割中提出了一种简单实用的自适应阈值法 ,将图像之间的相关性与区域生长法结合 ;后处理中使用形态学方法、多分辨率滤波等算法。完成了对所有 1733张图片的分割 ,经过与原图的对照 ,证明了所提出方法在分割中的准确性和较广泛的适用性     

CT图像肺结节自动检测

肺癌是对人类生命健康危害最大的恶性肿瘤之一。计算机辅助诊断系统对肺部CT图像进行自动分析后,可提示医生可疑肺结节,从而克服医生在诊断中的一些主观因素,为此本文提出了一种基于胸部CT图像的可疑肺结节自动检测算法。首先,根据胸部组织的特殊结构,利用一种新的分割算法提取出肺实质部分;在此基础上提取出灰度与结节相近的感兴趣区域,包括结节、肺血管、支气管;然后,以已标记的结节数据作为样本集,计算结节的面积、灰度均值、灰度方差、圆形度、形状矩、体积、球形度等特征值,利用最近邻法建立分类器判别函数;最后,计算测试集感兴趣区域的上述特征,对其进行判别、分类,并标记出结节。试验结果表明,该算法综合考虑了肺结节特征,具有较高的准确度。     

Development of Automated Image Stitching System for Radiographic Images

Standard X-ray images using conventional screen-film technique have a limited field of view that is insufficient to show the full bone structure of large hands on a single frame. To produce images containing the whole hand structure, digitized images from the X-ray films can be assembled using image stitching. This paper presents a new medical image stitching method that utilizes minimum average correlation energy filters to identify and merge pairs of hand X-ray medical images. The effectiveness of the proposed method is demonstrated in the experiments involving two databases which contain a total of 40 pairs of overlapping and non-overlapping hand images. The experimental results are compared with that of the normalized cross-correlation (NCC) method. It is found that the proposed method outperforms the NCC method in classifying and merging the overlapping and non-overlapping medical images. The efficacy of the proposed method is further indicated by its average execution time, which is about five times shorter than that of the other method.     

Fully Automated Deep Learning System for Bone Age Assessment

Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.     

An Automated System for Heart Contour Delineation in Photofluorographic Images

An automated system for heart contour delineation in photofluorographic images is described. This system allows the heart contour to be isolated without distortion. The heart contour in photofluorographic image is isolated using a decomposition method for empirical modes. The isolation algorithm, which is based on textural segmentation, is promising for various applications.     

A String Matching Computer-Assisted System for Dolphin Photoidentification

This paper presents a syntactic/semantic string representation scheme as well as a string matching method as part of a computer-assisted system to identify dolphins from photographs of their dorsal fins. A low-level string representation is constructed from the curvature function of a dolphin's fin trailing edge, consisting of positive and negative curvature primitives. A high-level string representation is then built over the low-level string via merging appropriate groupings of primitives in order to have a less sensitive representation to curvature fluctuations or noise. A family of syntactic/semantic distance measures between two strings is introduced. A composite distance measure is then defined and used as a dissimilarity measure for database search, highlighting both the syntax (structure or sequence) and semantic (attribute or feature) differences. The syntax consists of an ordered sequence of significant protrusions and intrusions on the edge, while the semantics consist of seven attributes extracted from the edge and its curvature function. The matching results are reported for a database of 624 images corresponding to 164 individual dolphins. The identification results indicate that the developed string matching method performs better than the previous matching methods including dorsal ratio, curvature, and curve matching. The developed computer-assisted system can help marine mammalogists in their identification of dolphins, since it allows them to examine only a handful of candidate images instead of the currently used manual searching of the entire database. © 2000 Biomedical Engineering Society. PAC00: 8780Tq, 4230Sy, 0705Pj     

Image Analysis for Cystic Fibrosis: Computer-Assisted Airway Wall and Vessel Measurements from Low-Dose, Limited Scan Lung CT Images

Cystic fibrosis (CF) is a life-limiting genetic disease that affects approximately 30,000 Americans. When compared to those of normal children, airways of infants and young children with CF have thicker walls and are more dilated in high-resolution computed tomographic (CT) imaging. In this study, we develop computer-assisted methods for assessment of airway and vessel dimensions from axial, limited scan CT lung images acquired at low pediatric radiation doses. Two methods (threshold- and model-based) were developed to automatically measure airway and vessel sizes for pairs identified by a user. These methods were evaluated on chest CT images from 16 pediatric patients (eight infants and eight children) with different stages of mild CF related lung disease. Results of threshold-based, corrected with regression analysis, and model-based approaches correlated well with both electronic caliper measurements made by experienced observers and spirometric measurements of lung function. While the model-based approach results correlated slightly better with the human measurements than those of the threshold method, a hybrid method, combining these two methods, resulted in the best results.     

CAVASS: A Computer-Assisted Visualization and Analysis Software System

The Medical Image Processing Group at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open-source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available and open source, and it is integrated with toolkits such as Insight Toolkit and Visualization Toolkit. CAVASS runs on Windows, Unix, Linux, and Mac but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive clusters of work stations for more time-consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3-dimensional and higher-dimensional medical imagery, so support for digital imaging and communication in medicine data and the efficient implementation of algorithms is given paramount importance.     

Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment

We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2?±?10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists.

     

Computer-Assisted Bone Age Assessment: Graphical User Interface for Image Processing and Comparison

The current study is part of a project resulting in a computer-assisted analysis of a hand radiograph yielding an assessment of skeletal maturity. The image analysis is based on features selected from six regions of interest. At various stages of skeletal development different image processing problems have to be addressed. At the early stage, feature extraction is based on Lee filtering followed by the random Gibbs fields and mathematical morphology. Once the fusion starts, wavelet decomposition methods are implemented. The user interface displays the closest neighbors to each image under consideration. Results show the sensitivity of different regions to both stages of development and certain feature sensitivity within each region. At the early stage of development, the distal features are more reliable indicators, whereas at the stage of epiphyseal fusion, a larger dynamic range of middle features makes them more sensitive. In the current study, a graphical user interface has been designed and implemented for testing the image processing routines and comparing the results of quantitative image analysis with the visual interpretation of extracted regions of interest. The user interface may also serve as a teaching tool. At the later stage of the project it will be used as a classification tool.     

基于CT图像的肺结节计算机辅助诊断系统

本文介绍了一种基于CT图像的肺结节计算机辅助自动诊断系统。我们将肺结节的自动检测分为肺实质的提取、感兴趣区域（ROI）的分割和ROI特征参数提取及分类判别几个步骤。该系统能够在对肺部CT图像进行自动分析后给医生提示出可疑肺结节，从而提高了医疗诊断效率。     

MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling

Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269–1274, 2016; Arch Dis Child 81.2:172–173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks. Recently, the advent and proliferation of convolutional neural networks (CNNs) has shown promise in a variety of medical imaging applications. There have been at least two published applications of using deep learning for evaluation of bone age (Med Image Anal 36:41–51, 2017; JDI 1–5, 2017). However, current implementations are limited by a combination of both architecture design and relatively small datasets. The purpose of this study is to demonstrate the benefits of a customized neural network algorithm carefully calibrated to the evaluation of bone age utilizing a relatively large institutional dataset. In doing so, this study will aim to show that advanced architectures can be successfully trained from scratch in the medical imaging domain and can generate results that outperform any existing proposed algorithm. The training data consisted of 10,289 images of different skeletal age examinations, 8909 from the hospital Picture Archiving and Communication System at our institution and 1383 from the public Digital Hand Atlas Database. The data was separated into four cohorts, one each for male and female children above the age of 8, and one each for male and female children below the age of 10. The testing set consisted of 20 radiographs of each 1-year-age cohort from 0 to 1 years to 14–15+?years, half male and half female. The testing set included left-hand radiographs done for bone age assessment, trauma evaluation without significant findings, and skeletal surveys. A 14 hidden layer-customized neural network was designed for this study. The network included several state of the art techniques including residual-style connections, inception layers, and spatial transformer layers. Data augmentation was applied to the network inputs to prevent overfitting. A linear regression output was utilized. Mean square error was used as the network loss function and mean absolute error (MAE) was utilized as the primary performance metric. MAE accuracies on the validation and test sets for young females were 0.654 and 0.561 respectively. For older females, validation and test accuracies were 0.662 and 0.497 respectively. For young males, validation and test accuracies were 0.649 and 0.585 respectively. Finally, for older males, validation and test set accuracies were 0.581 and 0.501 respectively. The female cohorts were trained for 900 epochs each and the male cohorts were trained for 600 epochs. An eightfold cross-validation set was employed for hyperparameter tuning. Test error was obtained after training on a full data set with the selected hyperparameters. Using our proposed customized neural network architecture on our large available data, we achieved an aggregate validation and test set mean absolute errors of 0.637 and 0.536 respectively. To date, this is the best published performance on utilizing deep learning for bone age assessment. Our results support our initial hypothesis that customized, purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. We build on that initial work by showing that the addition of state-of-the-art techniques such as residual connections and inception architecture further improves prediction accuracy. This is important because the current assumption for use of residual and/or inception architectures is that a large pre-trained network is required for successful implementation given the relatively small datasets in medical imaging. Instead we show that a small, customized architecture incorporating advanced CNN strategies can indeed be trained from scratch, yielding significant improvements in algorithm accuracy. It should be noted that for all four cohorts, testing error outperformed validation error. One reason for this is that our ground truth for our test set was obtained by averaging two pediatric radiologist reads compared to our training data for which only a single read was used. This suggests that despite relatively noisy training data, the algorithm could successfully model the variation between observers and generate estimates that are close to the expected ground truth.     

Computer-Assisted Detection of Pulmonary Nodules: Preliminary Observations Using a Prototype System with Multidetector-Row CT Data Sets

The continued revolution in multidetector-row CT (MDCT) scanning increases the quality of lung imaging but at the cost of a greater burden of data for review and interpretation. This article discusses our preliminary experience with prototype software for lung nodule detection and characterization using MDCT data sets. We discuss the potential role of computer-assisted detection (CAD) as applied to the automatic detection of lung nodules. We also review the process of CAD, outline its potential results, and explore how it may fit into existing radiology practice. Finally, we discuss MDCT data-acquisition parameters and how they may affect the performance of CAD.     

Enhanced CT Images by the Wavelet Transform Improving Diagnostic Accuracy of Chest Nodules

The objective of this study was to compare the diagnostic accuracy in the interpretation of chest nodules using original CT images versus enhanced CT images based on the wavelet transform. The CT images of 118 patients with cancers and 60 with benign nodules were used in this study. All images were enhanced through an algorithm based on the wavelet transform. Two experienced radiologists interpreted all the images in two reading sessions. The reading sessions were separated by a minimum of 1 month in order to minimize the effect of observer’s recall. The Mann–Whitney U nonparametric test was used to analyze the interpretation results between original and enhanced images. The Kruskal–Wallis H nonparametric test of K independent samples was used to investigate the related factors which could affect the diagnostic accuracy of observers. The area under the ROC curves for the original and enhanced images was 0.681 and 0.736, respectively. There is significant difference in diagnosing the malignant nodules between the original and enhanced images (z = 7.122, P < 0.001), whereas there is no significant difference in diagnosing the benign nodules (z = 0.894, P = 0.371). The results showed that there is significant difference between original and enhancement images when the size of nodules was larger than 2 cm (Z = −2.509, P = 0.012, indicating the size of the nodules is a critical evaluating factor of the diagnostic accuracy of observers). This study indicated that the image enhancement based on wavelet transform could improve the diagnostic accuracy of radiologists for the malignant chest nodules.Key words: Wavelet transform, chest nodules, enhanced CT     

牙颌CT图像序列中牙的半自动分割方法

牙颌CT图像序列相邻切片之间,相应牙的大小、位置以及牙区域和轮廓的灰度分布等特征比较接近,并呈一定的变化规律,根据这一特点提出了牙颌CT图像序列中牙的半自动分割方法。首先选取参考切片,加入少量用户操作进行参考切片中牙轮廓的提取,接着以参考切片为起始切片,由已完成轮廓提取的牙包围盒作为待处理切片(相邻切片)相应牙的操作区间,然后在此区间内用区域生长法提取牙轮廓,由此逐张切片处理可以自动地得到所有切片全牙列每颗牙的轮廓。实验结果表明,本方法仅需少量用户交互就能快速、基本准确地从牙颌CT图像序列中分割出牙轮廓,具有一定的实用价值。     

Correlation of PET/CT Standardized Uptake Value Measurements Between Dedicated Workstations and a PACS-Integrated Workstation System

Purpose This study was conducted to evaluate the clinical utility of a Positron Emission Tomography/Computed Tomography (PET/CT) analysis module of a picture archiving communication system (PACS) workstation in comparison to a dedicated PET/CT interpretation workstation. Materials and Methods The study included 32 consecutive patients referred for an [¹⁸F] Fluro-2-Deoxy-D-Glucose (¹⁸F-FDG) PET/CT at our institution. Images were reviewed at dedicated PET/CT and at PACS-integrated workstations. Mean standardized uptake values (SUVs) were calculated for the liver and the lung. Maximum SUVs were recorded for the bladder and an index lesion with the highest FDG uptake. The time spent for SUV measurements was recorded. Correlation of the SUV measurements was calculated with the Pearson coefficient. Results Pearson coefficients between the workstations ranged from 0.96 to 0.99 for bladder and lesion maximum SUVs. For liver and lung average SUVs, the coefficients varied from 0.53 to 0.98. The mean time spent to perform the four SUV measurements was 122.6 s for the dedicated workstations and 134.6 s for the PACS-integrated system. Conclusion The correlation of SUV measurements between dedicated PET/CT and PACS-integrated workstations is very good, especially for maximum SUVs. For routine reading of PET/CT scans, a PACS workstation with a PET/CT analysis module offers an excellent alternative to the use of a dedicated PET/CT workstation.     

Binary Segmentation Masks Can Improve Intrasubject Registration Accuracy of Bone Structures in CT Images

Registration of bone structures is a common problem in medical research as well as in clinical applications. Intrasubject rigid 3D monomodality registration of segmented bone structures of CT images and multimodality registration of μMR and segmented μCT bone images were performed with the multiresolution intensity-based technique implemented in ITK. The registration results for binary volumes of interest (VOI) masks and for segmented gray value VOIs were compared. To determine the registration quality, in the monomodality case the sum of squared difference, the sum of absolute differences, and the normalized symmetric difference of binary masks and in the multimodality case Mattes mutual information were applied. The use of binary VOI masks was significantly superior to the use of gray value VOIs.     

Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images

Journal of Digital Imaging - Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has...     

CT结肠造影中息肉的自动识别

目的：通过计算机自动识别CT结肠造影中结肠息肉的方法，提高结肠息肉筛查的灵敏度及效率。方法：利用计算CT图像的偏导数得到等值面的几何形态，找到符合息肉特征的体素，通过区域生长及模糊分类完成对息肉的识别与分割，并将识别到的息肉用特殊颜色标记，在其引导下进行虚拟内窥镜重建。结果：自动识别方法对息肉的总体敏感度为58．8％，假阳性率为4．7个，病例，平均检查时间为15．3min。同人工方法相比，检查时间平均缩短40．5％，且对5-10mm息肉的敏感度有明显提高（P=0．046）。结论：本方法具有较高的识别灵敏度，能加快息肉筛查的速度，可作为结肠息肉筛查的辅助手段。