Computer-aided detection of lung nodules based on decision fusion techniques

We adopted decision fusion techniques to develop a computer-aided detection (CAD) system for automatic detection of pulmonary nodules in low-dose CT images. Two distinct phases, aimed, respectively, at detecting volumes of interests (VOIs) within the CT scan, and at classifying VOIs into nodules and non-nodules, were considered. Three algorithms, namely thresholding, region growing and robust fuzzy clustering, were used as VOI detectors. For the classification phase, we built multi-classifier systems, which aggregate the decisions of three statistical classifiers, a neural network and a decision tree. Finally, the receiver operating characteristic convex hull method was used to build the final classifier, which results to be the aggregation of the best local behaviors of both classifiers and combiners. All the CAD modules were tested on CT scans analyzed by two expert radiologists. In the experiments, we achieved a sensitivity of 92.5% against a specificity of 83.5%.     

应用于CT图像肺结节检测的深度学习方法综述

肺癌是世界上死亡率最高的癌症，通过胸部CT影像检测肺结节对肺癌早期诊断和治疗意义重大。为了减轻放射科医生的工作量以及同时减少误诊率和漏诊率，研究人员提出了计算机辅助检测（CAD）系统辅助放射科医生检测和诊断肺结节。目前，研究人员正在尝试不同的深度学习技术，以提高计算机辅助诊断系统在基于CT图像的肺癌筛查中的性能。这项工作回顾了作为肺癌检测的CAD系统目前典型的深度学习的算法和框架，主要从数据集介绍、2D深度学习方法、3D深度学习方法、数据不平衡问题的处理、模型训练方法以及模型可解释性这六个方面进行介绍。最后，对各个方法的主要特点和算法性能进行了综合比较分析，并对如何提高结节检测性能进行了展望。     

3D多尺度深度卷积神经网络肺结节检测

目的 肺结节是肺癌的早期存在形式。低剂量CT（computed tomogragphy）扫描作为肺癌筛查的重要检查手段,已经大规模应用于健康体检,但巨大的CT数据带来了大量工作,随着人工智能技术的快速发展,基于深度学习的计算机辅助肺结节检测引起了关注。由于肺结节尺寸差别较大,在多个尺度上表示特征对结节检测任务至关重要。针对结节尺寸差别较大导致的结节检测困难问题,提出一种基于深度卷积神经网络的胸部CT序列图像3D多尺度肺结节检测方法。方法 包括两阶段：1）尽可能提高敏感度的结节初检网络;2）尽可能减少假阳性结节数量的假阳性降低网络。在结节初检网络中,以组合了压缩激励单元的Res2Net网络为骨干结构,使同一层卷积具有多种感受野,提取肺结节的多尺度特征信息,并使用引入了上下文增强模块和空间注意力模块的区域推荐网络结构,确定候选区域;在由Res2Net网络模块和压缩激励单元组成的假阳性降低网络中对候选结节进一步分类,以降低假阳性,获得最终结果。结果 在公共数据集LUNA16（lung nodule analysis 16）上进行实验,实验结果表明,对于结节初检网络阶段,当平均每例假阳性个数为22时,敏感度可达到0.983,相比基准ResNet + FPN（feature pyramid network）方法,平均敏感度和最高敏感度分别提高了2.6%和0.8%;对于整个3D多尺度肺结节检测网络,当平均每例假阳性个数为1时,敏感度为0.924。结论 与现有主流方案相比,该检测方法不但提高了肺结节检测的敏感度,还有效地控制了假阳性,取得了更优的性能。     

基于局部灰度最大和改进Mahalanobis

CT图像中肺结节检测一直是肺癌CAD系统的关键和难点。提出了一种孤立性肺结节自动检测算法,首先对原始CT图像进行有效、准确的肺实质分割;采用寻找局部灰度最大值方法对ROI进行初始分割;再对分割出的各ROI进行特征提取,利用SVM方法对每个特征进行定量描述,根据SVM单特征分类准确率对Mahalanobis距离进行加权改进,最后采用基于改进的Mahalanobis距离进行肺结节分类。实验结果表明,该算法可以较好地提取出CT图像中的孤立性肺结节,具有较高的灵敏度和较低的漏诊率,可以为医生诊断早期肺癌病灶提供帮助信息。     

Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques

A computer-aided diagnostic (CAD) system for effective and accurate pulmonary nodule detection is required to detect the nodules at early stage. This paper proposed a novel technique to detect and classify pulmonary nodules based on statistical features for intensity values using support vector machine (SVM). The significance of the proposed technique is, it uses the nodules features in 2D & 3D and also SVM for the classification that is good to classify the nodules extracted from the image. The lung volume is extracted from Lung CT using thresholding, background removal, hole-filling and contour correction of lung lobe. The candidate nodules are extracted and pruned using the rules based on ground truth of nodules. The statistical features for intensity values are extracted from candidate nodules. The nodule data are up-samples to reduce the biasness. The classifier SVM is trained using data samples. The efficiency of proposed CAD system is tested and evaluated using Lung Image Consortium Database (LIDC) that is standard data-set used in CAD Systems for Lungs Nodule classification. The results obtained from proposed CAD system are good as compare to previous CAD systems. The sensitivity of 96.31% is achieved in the proposed CAD system.     

基于局部灰度最大和改进Mahalanobis距离分类的肺结节检测算法

CT图像中肺结节检测一直是肺癌CAD系统的关键和难点。提出了一种孤立性肺结节自动检测算法,首先对原始CT图像进行有效、准确的肺实质分割;采用寻找局部灰度最大值方法对ROI进行初始分割;再对分割出的各ROI进行特征提取,利用SVM方法对每个特征进行定量描述,根据SVM单特征分类准确率对Mahalanobis距离进行加权改进,最后采用基于改进的Mahalanobis距离进行肺结节分类。实验结果表明,该算法可以较好地提取出CT图像中的孤立性肺结节,具有较高的灵敏度和较低的漏诊率,可以为医生诊断早期肺癌病灶提供帮助信息。     

New models for region of interest reader classification analysis in chest radiographs

In several computer-aided diagnosis (CAD) applications of image processing, there is no sufficiently sensitive and specific method for determining what constitutes a normal versus an abnormal classification of a chest radiograph. In the case of lung nodule detection or in classifying the perfusion of pneumoconiosis, multiple radiograph readers (radiologists) are asked to examine and score specific regions of interest (ROIs). The readers provide size, shape and perfusion grades for the presence of opacities in each region and then use all the ROI grades to classify the lung as normal or abnormal. The combined grades from all readers are then used to arrive at a consensus normal or abnormal classification. In this paper, using area under the ROC curve, we evaluate new mathematical models that are based on mathematical statistics, logic functions, and several statistical classifiers to analyze reader performance in grading chest radiographs for pneumoconiosis as the first step toward applying this technique to early detection of nodules found in lung cancer. In pneumoconiosis, rounded opacities are on the order of 1-10 mm in size, while lung nodules are often not diagnosed until they reach a size on the order of 1 cm.     

Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs

The aim of this paper is to describe three emerging computer-aided diagnosis (CAD) systems induced by Japanese health care needs. CAD has been developing fast in the last two decades. The idea of using a computer to help in medical image diagnosis is not new. Some pioneer studies are dated back to the 1960s. In 1998, the first U.S. FDA (Food and Drug Administration) approved commercial CAD system, a film-digitized mammography system, was launched by R2 Technologies, Inc. The success was quickly repeated by a number of companies. The approval of Medicare CAD reimbursement in the U.S. in 2001 further boosted the industry. Today, CAD has its significance in the economy of the medical industry. FDA approved CAD products in the field of breast imaging (mammography, ultrasonography and breast MRI) and chest imaging (radiography and CT) can be seen. In Japan, as part of the “Knowledge Cluster Initiative” of the government, three computer-aided diagnosis (CAD) projects are hosted at the Gifu University since 2004. These projects are regarding the development of CAD systems for the early detection of (1) cerebrovascular diseases using brain MRI and MRA images by detecting lacunar infarcts, unruptured aneurysms, and arterial occlusions; (2) ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy using retinal fundus images; and (3) breast cancers using ultrasound 3-D volumetric whole breast data by detecting the breast masses. The projects are entering their final development stage. Preliminary results are presented in this paper. Clinical examinations will be started soon, and commercialized CAD systems for the above subjects will appear by the completion of this project.     

Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs

The aim of this paper is to describe three emerging computer-aided diagnosis (CAD) systems induced by Japanese health care needs. CAD has been developing fast in the last two decades. The idea of using a computer to help in medical image diagnosis is not new. Some pioneer studies are dated back to the 1960s. In 1998, the first U.S. FDA (Food and Drug Administration) approved commercial CAD system, a film-digitized mammography system, was launched by R2 Technologies, Inc. The success was quickly repeated by a number of companies. The approval of Medicare CAD reimbursement in the U.S. in 2001 further boosted the industry. Today, CAD has its significance in the economy of the medical industry. FDA approved CAD products in the field of breast imaging (mammography, ultrasonography and breast MRI) and chest imaging (radiography and CT) can be seen. In Japan, as part of the "Knowledge Cluster Initiative" of the government, three computer-aided diagnosis (CAD) projects are hosted at the Gifu University since 2004. These projects are regarding the development of CAD systems for the early detection of (1) cerebrovascular diseases using brain MRI and MRA images by detecting lacunar infarcts, unruptured aneurysms, and arterial occlusions; (2) ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy using retinal fundus images; and (3) breast cancers using ultrasound 3-D volumetric whole breast data by detecting the breast masses. The projects are entering their final development stage. Preliminary results are presented in this paper. Clinical examinations will be started soon, and commercialized CAD systems for the above subjects will appear by the completion of this project.     

Top-hat和Gabor filter在肺结节ROI自动检测中的应用研究

在自动诊断大量带有病变区域的CT图像时,计算机辅助诊断起着重要的作用。提出了一种自动检测肺结节感兴趣区域的方法。对肺实质进行分割;利用Top-hat滤波提取包含血管和结节在内的初始感兴趣区域;用Gabor filter对图像进行第二次处理;对图像进行比对,从而得到更为精确的疑似结节的病灶区域。实验证明该方法能准确完整地提取出感兴趣区域。     

基于目标检测算法的肺结节辅助诊断系统

据统计,肺癌在全世界范围内是发病率、致死率最高的疾病之一。随着计算机辅助诊断系统（CAD）和卷积神经网络（CNN）的成熟化,医疗领域的诊断治疗也逐渐智能化。本文提出一种基于目标检测算法的肺结节自动检测方法,并提出一套将阈值分割算法和数字形态学处理相结合的肺实质CT影像处理流程。对LUNA16数据集中的1186个肺结节进行训练和学习,观察YOLO V3模型在数据集中的评价结果来验证模型,实验结果准确率达到92.18%,每张图片平均检测时间为0.035 s。与现有的肺结节检测算法SSD、CNN、U-Net等模型进行对比试验,以验证YOLO V3模型的有效性。同时本文基于CAD技术设计肺结节辅助诊断系统,实现人机交互,为医生提供简单明了的辅助诊断工具。     

STL rapid prototyping bio-CAD model for CT medical image segmentation

This paper presents a simple process to construct 3D rapid prototyping (RP) physical models for computer tomography (CT) medical images segmentation. The use of stereolithography (STL) triangular meshes as a basis for RP construction facilitates the simplification of the process of converting CT images to an RP model. This is achieved by constructing the STL triangular meshes directly from data points without having to draw the curve model first. The grey prediction algorithm is used to sort contour point data in each layer of the medical image. The contour difference detection operation is used to sequence the points for each layer. The 3D STL meshes are then constructed by this proposed layer-by-layer sequence meshes algorithm to build the STL file. Once this STL file is saved, a 3D physical model of the medical image can be fabricated by RP manufacturing, and its virtual reality model can also be presented for visualization. CT images of a human skull and femur bone were used as the case studies for the construction of the 3D solid model with medical images. The STL models generated using this new methodology were compared to commercial computer-aided design (CAD) models. The results of this comparative analysis show that this new methodology is statistically comparable to that of the CAD software. The results of this research are therefore clinically reliable in reconstructing 3D bio-CAD models for CT medical images.     

基于三维各向异性卷积的肺结节良恶性分类

计算机断层扫描（Computer Tomography,CT）图像中肺结节的良恶性诊断对治疗方案的选择有非常重要的作用。目前基于深度学习的CT图像肺结节良恶性分类算法的一个研究趋势是充分利用CT图像的三维信息来设计网络,但由于不同CT设备采集的图像参数不同,不同样本的CT图像其层内及层间分辨率也多不相同,进行特征提取前需要进行额外的预处理工作。大多数文献的做法是采用插值的方法统一分辨率,然而这种方法会造成图像分辨率降低或计算量增加等问题。针对这一问题,提出了一种基于三维各向异性卷积的肺结节良恶性分类网络,通过将标准三维卷积拆分为[k×k×1]和[1×1×k]的两种三维各向异性卷积,避免了直接将三维卷积作用到原始CT图像上,从而避免了图像分辨率不同的影响。还提出了裁剪-非局部池化模块,通过中心裁剪和非局部池化操作,强化网络对结节区域的特征提取,同时使浅层网络也可以获取全局信息。在Lung Image Database Consortium and Image Database Resource Initiative（LIDC-IDRI）数据集上的实验表明,提出的三维各向异性卷积结合裁剪-非局部池化操作的神经网络能显著减少网络参数量,提升网络提取特征的能力,实现对肺结节良恶性的准确分类,分类的准确率、敏感性、特异性分别为91.53%、88.89%和93.27%,取得了比较好的分类性能。     

Hessian based approaches for 3D lung nodule segmentation

In the design of computer-aided diagnosis systems for lung cancer diagnosis, an appropriate and accurate segmentation of the pulmonary nodules in computerized tomography (CT) is one of the most relevant and difficult tasks. An accurate segmentation is crucial for the posterior measurement of nodule characteristics and for lung cancer diagnosis.This paper proposes different approaches that use Hessian-based strategies for lung nodule segmentation in chest CT scans. We propose a multiscale segmentation process that uses the central medialness adaptive principle, a Hessian-based strategy that was originally formulated for tubular extraction but it also provides good segmentation results in blob-like structures as is the case of lung nodules. We compared this proposal with a well established Hessian-based strategy that calculates the Shape Index (SI) and Curvedness (CV). We adapted the SI and CV approach for multiscale nodule segmentation. Moreover, we propose the combination of both strategies by combining the results, in order to take benefit of the advantages of both strategies.Different cases with pulmonary nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were taken and used to analyze and validate the approaches. The chest CT images present a large variability in nodule characteristics and image conditions. Our proposals provide an accurate lung nodule segmentation, similar to radiologists performance. Our Hessian-based approaches were validated with 569 solid and mostly solid nodules demonstrating that these novel strategies have good results when compared with the radiologists segmentations, providing accurate pulmonary nodule volumes for posterior characterization and appropriate diagnosis.     

肺结节球表面网格向量化特征分类

目的 基于球谐函数与容斥映射算法向量化球面表面纹理与结节形状用以进行胸部CT图像肺结节良恶性判定。区别于基于深度学习解决肺结节良恶性筛查的方法,目前方法多集中于框架改进而忽略了数据预处理,文中所提方法旨在对球面纹理与结节形状进行向量表达,使其可以输入深度森林进行特征分类训练。方法 首先采用辽宁中医药大学附属医院数据,通过3维重构获得3维肺结节图像。其次使用球谐函数与容斥映射算法在保留空间信息的同时将纹理以网格方式映射到标准球面上。再次使用网格-LBP与映射形变能量分别完成对球面纹理与结节形状信息的构建。最后提出一种基于网格的多粒度扫描方法对深度森林训练框架进行改进,并将向量化后的纹理和形状特征加入到改进的深度森林训练框架中进行实验验证。结果 通过大量的实验结果验证,在准确率（ACC）、特异度（SPE）、敏感度（SEN）和受试者工作特征曲线下的面积（AUC）4个衡量指标下,本文方法具有优于现存先进方法的表现,其中ACC、SPE、SEN和AUC分别达到76.06%、69.46%、88.46%和0.84。结论 基于球谐函数与容斥映射算法可成功地对肺结节表面和形状两个特征进行向量化并训练,不仅考虑了数据预处理,而且通过两个特征对肺结节良恶性检测的准确率要高于传统1个特征检测的结果,同时也为3维模型中特征的提取及向量化提供了一个有效的方法。     

Dual-branch residual network for lung nodule segmentation

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: (1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; (2) we combine the features of the intensity and the convolutional neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC-IDRI dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with the dice similarity coefficient (DSC) of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our DSC was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.     

A New Method for Encrypting Images with Few Details Using Rijndael and RC6 Block Ciphers in the Electronic Code Book Mode

ABSTRACT

In this paper, we propose an efficient method for encrypting images with few details using Rijndael and RC6 block ciphers in Electronic Code Book (ECB) mode. Images with few details are images with large areas of similar gray levels such as medical images, infrared images, and logo images. This leads to encryption leakage if the Rijndael or RC6 block ciphers are used. The proposed method solves this problem by using a preprocessing step to eliminate the repeated patterns before encryption. A comparison is held between encryption of images with few details with preprocessing and encryption without preprocessing. Experimental results verify that the proposed preprocessing method gives the encryption algorithms the ability to encrypt images with few details in an efficient manner in the ECB mode.     

基于特征金字塔网络的肺结节检测

针对计算机断层扫描（CT）影像中肺结节尺寸变化较大、尺寸小且不规则等特点导致的检测敏感度较低的问题,提出了基于特征金字塔网络（FPN）的肺结节检测方法。首先,利用FPN提取结节的多尺度特征,并强化小目标及目标边界细节的特征;其次,在FPN的基础上设计语义分割网络（名为掩模特征金字塔网络（Mask FPN））用于快速准确地分割提取肺实质,作为目标候选区域定位图像;并且,在FPN顶层添加反卷积层,采用多尺度预测策略改进快速区域卷积神经网络（Faster R-CNN）以提高检测性能;最后,针对肺结节数据集的正负样本不平衡问题,在区域候选网络（RPN）模块采用焦点损失函数以提高结节的检出率。所提方法在公开数据集LUNA16上进行实验,结果表明,利用FPN和反卷积层改进的新网络对结节检测效果有一定的帮助,采用焦点损失函数也有一定效果。综合多种改进,当平均每个扫描件的候选结节数为46.7时,所提方法的肺结节检测敏感度指标为95.7%,与其他卷积神经网络方法如Faster R-CNN、UNet等相比,具有较高的敏感性。所提方法能够较好地提取不同尺度上的结节特征,提高CT图像肺结节检测的敏感度,同时对于较小的结节也能有效检测,能更有效地辅助肺癌的诊断治疗。     

应用规则的肺结节识别系统

为了检测胸部CT图像中的肺结节,提出一种基于应用规则的自动识别肺结节的系统。在识别系统中通过自动阈值法和轮廓跟踪法分割肺实质;采用OTSU算法分割肺实质中的感兴趣区域,对感兴趣区域的特征进行提取;选择对肺结节和血管区别度较大的特征。根据选取的这些特征设定识别肺结节的规则来确定肺结节的候选区域。实验结果表明,该系统对直径1 cm以上的结节具有较好的识别性能。