Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images.

A multi-criterion algorithm for automatic delineation of small pulmonary nodules on helical CT images has been developed. In a slice-by-slice manner, the algorithm uses density, gradient strength, and a shape constraint of the nodule to automatically control segmentation process. The multiple criteria applied to separation of the nodule from its surrounding structures in lung are based on the fact that typical small pulmonary nodules on CT images have high densities, show a distinct difference in density at the boundary, and tend to be compact in shape. Prior to the segmentation, a region-of-interest containing the nodule is manually selected on the CT images. Then the segmentation process begins with a high density threshold that is decreased stepwise, resulting in expansion of the area of nodule candidates. This progressive region growing approach is terminated when subsequent thresholds provide either a diminished gradient strength of the nodule contour or significant changes of nodule shape from the compact form. The shape criterion added to the algorithm can effectively prevent the high density surrounding structures (e.g., blood vessels) from being falsely segmented as nodule, which occurs frequently when only the gradient strength criterion is applied. This has been demonstrated by examples given in the Results section. The algorithm's accuracy has been compared with that of radiologist's manual segmentation, and no statistically significant difference has been found between the nodule areas delineated by radiologist and those obtained by the multi-criterion algorithm. The improved nodule boundary allows for more accurate assessment of nodule size and hence nodule growth over a short time period, and for better characterization of nodule edges. This information is useful in determining malignancy status of a nodule at an early stage and thus provides significant guidance for further clinical management.     

Quantitative CT: technique dependence of volume estimation on pulmonary nodules

Current estimation of lung nodule size typically relies on uni- or bi-dimensional techniques. While new three-dimensional volume estimation techniques using MDCT have improved size estimation of nodules with irregular shapes, the effect of acquisition and reconstruction parameters on accuracy (bias) and precision (variance) of the new techniques has not been fully investigated. To characterize the volume estimation performance dependence on these parameters, an anthropomorphic chest phantom containing synthetic nodules was scanned and reconstructed with protocols across various acquisition and reconstruction parameters. Nodule volumes were estimated by a clinical lung analysis software package, LungVCAR. Precision and accuracy of the volume assessment were calculated across the nodules and compared between protocols via a generalized estimating equation analysis. Results showed that the precision and accuracy of nodule volume quantifications were dependent on slice thickness, with different dependences for different nodule characteristics. Other parameters including kVp, pitch, and reconstruction kernel had lower impact. Determining these technique dependences enables better volume quantification via protocol optimization and highlights the importance of consistent imaging parameters in sequential examinations.     

CT三维重建技术在解剖性肺段切除中的应用

目的 探讨CT三维重建技术在解剖性肺段切除中的应用价值.方法 2020年3月至2021年3月期间共有36例肺结节患者在江门市中心医院行胸腔镜解剖性肺段切除术.术前患者均行胸部增强CT,通过CT三维重建软件mimics进行支气管、血管三维成像,显示肺段的解剖结构,决定术中需解剖和切断的肺段动脉、静脉、支气管,制定手术方案.术中根据三维重建解剖暴露目标肺段的动脉、静脉、支气管,验证三维重建的准确性.结果 所有患者均顺利完成手术;术中情况和术前规划基本相符;本组手术时间(87.7±27.9)min,手术出血量(53.3±11.2)ml,术后引流量(563.6±116.3)ml,胸管留置时间(2.9±1.4)d,术后住院时间(5.1±1.3)d,术后均未出现严重并发症及手术死亡.结论 术前CT三维重建技术能清楚显示肺段的解剖结构,对精准肺段切除有其应用价值.     

基于PET/CT图像纹理参数的肺结节诊断模型

目的基于PET/CT融合图像纹理参数建立肺结节良恶性诊断模型,提高肺癌的识别率。方法选取宣武医院核医学科经PET/CT检查的52例肺结节患者,收集其PET/CT影像图像及人口学、影像学信息。以Contourlet变换和灰度共生矩阵相结合的方式,对PET/CT图像的感兴趣区域提取纹理参数。基于所提取的纹理参数建立支持向量机模型,得到每个肺结节良恶性判别结果。为了提高模型的诊断效果,将结节边缘、最大摄取值、有晕征等影像学信息也纳入模型,重新建立支持向量机模型。通过灵敏度、特异度、正确率等指标对模型诊断效果进行评价。结果纹理参数肺结节诊断模型的灵敏度、特异度分别为90.7%、93.5%,纹理参数结合影像学信息的肺结节诊断模型的灵敏度、特异度分别为95.7%、100.0%。结论基于PET/CT图像纹理参数建立的支持向量机模型对良恶性肺结节具有较好的鉴别诊断效果。     

Quantitative in vivo analysis of the kinematics of carpal bones from three-dimensional CT images using a deformable surface model and a three-dimensional matching technique

The purpose of this study was to obtain quantitative information of the relative displacements and rotations of the carpal bones during movement of the wrist. Axial helical CT scans were made of the wrists of 11 volunteers. The wrists were imaged in the neutral position with a conventional CT technique, and in 15-20 other postures (flexion-extension, radial-ulnar deviation) with a low-dose technique. A segmentation of the carpal bones was obtained by applying a deformable surface model to the regular-dose scan. Next, each carpal bone, the radius, and ulna in this scan was registered with the corresponding bone in each low-dose scan using a three-dimensional matching technique. A detailed definition of the surfaces of the carpal bones was obtained from the regular-dose scans. The low-dose scans provided sufficient information to obtain an accurate match of each carpal bone with its counterpart in the regular-dose scan. Accurate estimates of the relative positions and orientations of the carpal bones during flexion and deviation were obtained. This quantification will be especially useful when monitoring changes in kinematics before and after operative interventions, like mini-arthrodeses. This technique can also be applied in the quantification of the movement of other bones in the body (e.g., ankle and cortical spine).     

Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images

We propose a new curvature-based method for correcting the segmented lung boundary. Our method consists of the following steps. First, the lungs are extracted from chest CT images by the automatic segmentation method. Second, the segmented lung contours are corrected by lung smoothing in each axial slice. Our scan line search provides an efficient contour tracing and curvature calculation. Finally, the smoothed lung contours are corrected by 3D VOI refinement. This increases the smoothness in the z-axis without distortion of the lung boundary. Experimental results show that our method effectively incorporates the pleural nodules and pulmonary vessels into the segmentation results.     

基于双源CT图像的冠状动脉分割

背景：在计算机辅助下,从双源CT图像中把三维冠状动脉分割出来能为其定量评价提供基础。但冠状动脉的三维形态复杂多变,且其管径细小,因而实现冠状动脉的高精度分割是一项有挑战性的课题。 目的：解决冠状动脉难以实现高精度分割的问题。 方法：采用三步数据处理策略实现冠状动脉分割。先采用阈值方法对三维双源CT图像进行预分割;然后,采用交互式的策略分割出与主动脉相连的左、右冠状动脉始端;最后,根据冠状动脉始端的位置,利用形态学方法和三维断层图像相邻层间的关系分割出三维冠状动脉。 结果与结论：提出的基于形态学与断层图像层间关系的分割方法能较精确地从双源CT图像中分割出左、右冠状动脉,说明该方法适用于三维冠状动脉的分割。     

Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images

An automated computerized scheme has been developed for determination of the likelihood measure of malignancy of pulmonary nodules on low-dose helical CT (LDCT) images. Our database consisted of 76 primary lung cancers (147 slices) and 413 benign nodules (576 slices). With this automated computerized scheme, the location of a nodule was first indicated by a radiologist. The outline of the nodule was segmented automatically by use of a dynamic programming technique. Various objective features on the nodules were determined by use of outline analysis and image analysis, and the likelihood measure of malignancy was determined by use of linear discriminant analysis (LDA). The effect of many different combinations of features and the performance of LDA in distinguishing benign nodules from malignant ones were evaluated by means of receiver operating characteristic (ROC) analysis. The Az value (area under the ROC curve) obtained by the computerized scheme in distinguishing benign nodules from malignant ones was 0.828 when a single slice was employed for each of the nodules. However, the Az value was improved to 0.846 when multiple slices were used for determination of the likelihood measure of malignancy. The Az values obtained by the computerized scheme on LDCT images were significantly greater than the Az value of 0.70, which was obtained from our previous observer studies by radiologists in distinguishing benign nodules from malignant ones on LDCT images. The automated computerized scheme for determination of the likelihood measure of malignancy would be useful in assisting radiologists to distinguish between benign and malignant pulmonary nodules on LDCT images.     

Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections

Currently, radiologists can fail to detect lung nodules in up to 30% of actually positive cases. If a computerized scheme could alert the radiologist to locations of suspected nodules, then potentially the number of missed nodules could be reduced. We are developing such a computerized scheme that involves a difference-image approach and various feature-extraction techniques. In this paper, we describe our use of digital morphological processing in the reduction of computer-identified false-positive detections. A feature-extraction technique, which includes the sequential application of nonlinear filters of erosion and dilation, is employed to reduce the camouflaging effect of ribs and vessels on nodule detection. This additional feature-extraction technique reduced the true-positive rate of the computerized scheme by 13% and the false-positive rate by 50%. In a comparison of the scheme with and without the additional feature-extraction technique, inclusion of the additional technique increased the detection sensitivity by about half at the level of three to four false-positive detections per chest image.     

基于肺部PET/CT图像不同纹理特征的K最近邻分类器

目的对PET/CT图像高维纹理参数进行降维,基于不同纹理参数建立肺结节良恶性的K最近邻(K-nearest neighbor,KNN)分类器,探究最佳建模方法,提高分类的准确率。方法采用回顾性研究的方式,收集52例首都医科大学宣武医院核医学科肺结节患者的PET/CT图像,对图像的感兴趣区域基于Contourlet变换提取灰度共生矩阵的纹理参数。对肺结节PET/CT图像的纹理参数首先采用单因素分析的方法,根据ROC曲线下面积筛选纹理参数,再对其进行主成分分析提取主要成分。基于主成分、根据ROC曲线筛选的纹理及原始纹理分别采用K最近邻分类算法建立肺结节良恶性的分类器,通过正确率、灵敏度、特异度、阳性预测值(positive predictive value,PPV)、阴性预测值(negative predictive value,NPV)、ROC曲线下面积(area under curve,AUC)这些指标评价分类效果。结果 PET/CT图像共提取1344个原始纹理参数,经单因素分析后筛选出89个纹理参数,对筛选后的纹理共提取11个主成分。基于主成分、筛选纹理、原始纹理的分类模型正确率分别为0.614、0.579、0.263;AUC分别为0.645、0.610、0.515。结论在主成分纹理、单因素分析筛选的纹理、原始纹理中,基于主成分纹理建立K最近邻分类器的效果最好。     

肺结节倍增时间的CT研究进展

目的 总结高分辨率CT测定不同性质肺结节的倍增时间对其临床随访管理决策的影响。方法 分别以“倍增时间”“生长速率”“肺结节”“早期肺癌”和“doubling time”“growth rate”“lung nodule”等为中英文关键词,在万方数据、中国知网、PubMed、Web of Science等中英文数据库中,检索1997年1月—2017年1月国内外有关倍增时间与肺结节关系的相关文献资料,并进行汇总分析。结果 不同大小、密度、病理类型的肺结节倍增时间不同,预后也不同。对于常规CT形态学和功能学检查无法判别良恶性质的肺结节,利用体积或质量倍增时间量化评估其生长速率,是提高早期肺癌诊断正确率和合理制定处理策略的手段之一;但是,肺结节倍增时间测定结果的准确度、具体方法及可重复性受多种因素影响,不同研究报道之间亦存在差异,需合理评估肺结节倍增时间的临床价值。结论 合理化随访肺结节的动态演变以及联合其倍增时间特点,可协助制定并完善临床综合管理决策,避免延误诊治或者过度治疗。     

基于MATLAB的CT肝脏肿瘤图像分割

目的 将CT图像中的肝脏肿瘤部分进行准确分割.方法 利用MATLAB平台对CT肝脏肿瘤图像进行预处理,并结合灰度转换、二值化处理、反色处理、形态学处理、区域生长法对病灶区域进行分割.结果 组合分割法能够发挥简单、快速、适合小病灶区域的分割特点,实现了肝脏肿瘤组织的分割,分割效果理想.结论 该方法用于肝部肿瘤的分割具有一定的有效性,但对于与周围粘连较多的肿瘤的分割还有局限性.     

Segmentation for CT images of liver neoplasms based on MATLAB

目的 将CT图像中的肝脏肿瘤部分进行准确分割.方法 利用MATLAB平台对CT肝脏肿瘤图像进行预处理,并结合灰度转换、二值化处理、反色处理、形态学处理、区域生长法对病灶区域进行分割.结果 组合分割法能够发挥简单、快速、适合小病灶区域的分割特点,实现了肝脏肿瘤组织的分割,分割效果理想.结论 该方法用于肝部肿瘤的分割具有一定的有效性,但对于与周围粘连较多的肿瘤的分割还有局限性.     

Improved contralateral subtraction images by use of elastic matching technique

A contralateral subtraction technique has been developed to assist radiologists in the detection of asymmetric abnormalities such as lung nodules on a single chest radiograph. With this technique, a contralateral subtraction image is obtained by subtracting a right/left reversed "mirror" image from the original one. The lesions in the subtraction image may be enhanced because most of the symmetric skeletal structures, such as peripheral ribs, are eliminated. Although the quality of the previous contralateral subtraction images is relatively good, severe misregistration artifacts, mainly due to serious asymmetry of the ribs in the two lungs of the original image, were observed in some cases, and minor misregistration artifacts were also observed in many cases. In this study, we employed three image warping techniques. An initial global warping technique was applied to reduce severe misregistration artifacts in the subtraction image caused by asymmetric rib structures. Additional two iterative warping techniques based on an elastic matching technique were used for accurate registration of the local structures of ribs, so that minor artifacts present in many subtraction images obtained with the previous technique were greatly reduced. With the new technique, the percentage of chest images, which were rated as being of adequate, good, or excellent quality of subtraction images by use of a subjective evaluation method, was improved from 91% to 97%. In particular, the number of cases with excellent quality was greatly increased from 15% to 42%. The contralateral subtraction technique can be used for detection of asymmetric abnormalities, such as lung nodules, pneumothorax, pneumonia, and emphysema, on peripheral lungs in single chest radiographs, and it therefore has potential utility in a large proportion of abnormal chest images.     

医学CT图像的几种后处理技术——VB6编程对CT影像的几种后处理

本文说明后处理的原理，并用VB6对五种主要的CT后处理技术编程，再应用于数字图形和医学CT影像，表现CT影像经后处理的特殊效果。     

CT图像肺结节计算机辅助检测技术研究进展

根据近年来国内外计算机辅助检测(computer-aided detection,CAD)技术在CT图像肺结节检测中的研究进展情况,本文对比分析了目前检测流程中6个阶段(图像采集、预处理、肺实质分割、感兴趣区域提取、特征提取与优化、检测分析与降低假阳性率)各自所运用的研究方法及问题,并提出肺结节检测准确率的提高,依赖于各步骤算法的优化和大样本标准病例数据库的建立,需要在研究针对单一类型结节分类算法的基础上,设计通用的结节分类算法。     

基于数学形态学的CT图像肝脏肿瘤提取研究

目的 肝脏肿瘤的提取是肝脏三维可视化、手术规划和模拟的基础,而当前肿瘤分割存在干预过多和分割效果不佳的问题.方法 本文通过对腹部CT图像进行高斯平滑以去除图像噪声和细密纹理,计算出图像的形态学梯度并用高、低帽变换进行增强,再根据用户选择点计算内部和外部标记符,然后基于控制标记符的分水岭算法分割图像,提取出腹部CT图像中的病变组织.结果 实验结果表明,该算法能够在较少的人工干预下快速分割出肝脏病变组织.结论 该算法实现了腹部CT图像中肝脏病变组织的提取.     

Segmentation of liver tumors for CT images based on mathematical morphology

目的 肝脏肿瘤的提取是肝脏三维可视化、手术规划和模拟的基础,而当前肿瘤分割存在干预过多和分割效果不佳的问题.方法 本文通过对腹部CT图像进行高斯平滑以去除图像噪声和细密纹理,计算出图像的形态学梯度并用高、低帽变换进行增强,再根据用户选择点计算内部和外部标记符,然后基于控制标记符的分水岭算法分割图像,提取出腹部CT图像中的病变组织.结果 实验结果表明,该算法能够在较少的人工干预下快速分割出肝脏病变组织.结论 该算法实现了腹部CT图像中肝脏病变组织的提取.     

Validation of a two- to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images.

We present a validation of an intensity based two- to three-dimensional image registration algorithm. The algorithm can register a CT volume to a single-plane fluoroscopy image. Four routinely acquired clinical data sets from patients who underwent endovascular treatment for an abdominal aortic aneurysm were used. Each data set was comprised of two intraoperative fluoroscopy images and a preoperative CT image. Regions of interest (ROI) were drawn around each vertebra in the CT and fluoroscopy images. Each CT image ROI was individually registered to the corresponding ROI in the fluoroscopy images. A cross validation approach was used to obtain a measure of registration consistency. Spinal movement between the preoperative and intraoperative scene was accounted for by using two fluoroscopy images. The consistency and robustness of the algorithm when using two similarity measures, pattern intensity and gradient difference, was investigated. Both similarity measures produced similar results. The consistency values were rotational errors below 0.74 degree and in-plane translational errors below 0.90 mm. These errors approximately relate to a two-dimensional projection error of 1.3 mm. The failure rate was less than 8.3% for three of the four data sets. However, for one of the data sets a much larger failure rate (28.5%) occurred.