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。     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Detection of factor Ⅷ intron 1 inversion in severe haemophilia A

Objective Screening the intron 1 inversion of factor Ⅷ (FⅧ) in the population of severe haemophilia A(HA) in China and performing carrier detection and prenatal diagnosis. Methods Using LD-PCR to detect intron 22 inversions and multiple-PCR within two tubes to intron 1 inversions in sereve HA patients. Carrier detection and prenatal diagnosis were performed in affected families. Linkage analysis and DNA sequencing were used to verify these tests. Results One hundred and eighteen patients were seven diagnosed as intron 22 inversions and 7 were intron 1 inversions out of 247 severe HA patients. The prevalence of the intron 1 inversion in Chinese severe haemophilia A patients was 2. 8% (7/247). Six women from family A and 2 from family B were diagnosed as carriers. One fetus from family A was affected fetus. Conclusion Intron 1 inversion could be detected directly by multiple-PCR within two tubes. This method made the strategy more perfective in carrier and prenatal diagnosis of haemophilia A.     

Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system

We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified by a k-means clustering technique. Each lung slice is classified as belonging to the upper, middle, or the lower part of the lung volume. Within each lung region, structures are segmented again using weighted k-means clustering. These structures may include true lung nodules and normal structures consisting mainly of blood vessels. Rule-based classifiers are designed to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We performed a preliminary study using 1454 CT slices from 34 patients with 63 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (53/63) with 5.48 (7961/1454) FP objects per slice. When rule-based classification was used before LDA, the free response receiver operating characteristic (FROC) curve improved over the entire sensitivity and specificity ranges of interest. In particular, the FP rate decreased to 1.74 (2530/1454) objects per slice at the same sensitivity. Thus, compared to FP reduction with LDA alone, the inclusion of rule-based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of our approach to lung nodule detection and FP reduction on CT images.     

Lung nodule detection in low-dose and thin-slice computed tomography

A computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan).     

胸片计算机辅助检测的研究进展

随着医学影像数字化的发展,对影像的智能化理解成为一种必然趋势,计算机辅助检测（computer-aided detection,CAD）系统已经成为了医学影像学研究的热点之一,并逐步进入了医学临床应用,这对于提高诊断准确率、减少漏诊有着非常积极的作用。文章在介绍CAD系统基本概念的基础上,重点阐述了CAD系统在胸片疾病诊断中的基本工作流程,包括病灶区域增强、肺区分割、可疑区域选择、病灶筛选和病灶特征的进一步分析,以及CAD系统在肺部结节检测、肺间质性病变检测、利用时间减影检测间期病理变化等方面的应用,并讨论了其发展趋势。     

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.     

Local Axial Scale-Attention for Universal Lesion Detection

Journal of Digital Imaging - Universal lesion detection (ULD) in computed tomography (CT) images is an important and challenging prerequisite for computer-aided diagnosis (CAD) to find abnormal...     

Effect of Multiscale Processing in Digital Chest Radiography on Automated Detection of Lung Nodule with a Computer Assistance System

The aim of this study is to evaluate the effect of multiscale processing in digital chest radiography on automated detection of lung nodule with a computer-aided diagnosis (CAD) system. The study involved 58 small-nodule patient cases and 58 normal cases. The 58 patient cases included a total of 64 noncalcified lung nodules up to 15 mm in diameter. Each case underwent an examination with a digital radiography system (Digital Diagnost, Philips Medical Systems), and the acquired image was processed by the following three types of multiscale processing (Unique Image Processing Package, Philips Medical Systems) respectively: (1) standard image from the default processing parameter (structure preference, 0.0), (2) high-pass image with structure preference of 0.4, (3) low-pass image with structure preference of ?0.4. The CAD output images were produced with a real-time computer assistance system (IQQA?-Chest, EDDA Technology). Two experienced chest radiologists established the nodule gold standard by consensus reading according to computed tomography results, and analyzed and recorded the detection of lung nodules and false-positive detections of these CAD output images. For the entire cases involved (each case with three types of different processing), a total of 348 observations were evaluated by the receiver operating characteristic (ROC) analysis. The mean area under the ROC curve (A _z ) value was 0.700 for the standard images, 0.587 for the high-pass images, and 0.783 for the low-pass images. There were statistically significant A _z values among these three types of processed images (p?<?0.01). Multiscale processing in digital chest radiography can affect the automated detection of lung nodule by CAD, which is consistent with effects from visual inspection.     

基于多特征融合跟踪的微小肺结节识别算法

如何在海量的肺部高分辨率CT(HRCT)序列图片中准确识别微小结节(直径为5～10 mm)一直是肺结节计算机辅助检测(CAD)系统的研究重点和难点。本文提出了一种新的微小肺结节识别算法——多特征融合跟踪算法。该算法在处理一个HRCT序列图片时,首先结合大津法和形态学方法获取每一张CT图的肺实质,再通过基于灰度阈值和改进的模板匹配算法提取感兴趣区域(ROI),接着计算ROI的多个有效特征,然后在整个HRCT序列图片中进行ROI的多特征跟踪和融合,最后根据分类规则识别并标出候选肺结节。实验证明,该算法能准确地检测出微小肺结节,且假阳率较低。     

肺部磨玻璃结节的CT特征与治疗策略进展

原发性肺癌是位居我国癌症死亡首位的恶性肿瘤,早发现并及时治疗的患者可以获得良好的效果。低剂量CT（LDCT）筛查的运用使得早期肺癌能够被及时发现,随着LDCT筛查的普及越来越多肺内磨玻璃结节（GGN）被发现。表现为GGN的周围型肺癌术前明确病理学诊断较为困难,而GGN的性质对于手术决策有重要参考意义,因此通过分析影像学特征来推断病变性质并作出合理的治疗策略十分重要。     

Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours

We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A(z)) of 0.83 +/- 0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries.     

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.