Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses

We study the performance of a computer-aided detection (CAD) system for lung nodules in computed tomography (CT) as a function of slice thickness. In addition, we propose and compare three different training methodologies for utilizing nonhomogeneous thickness training data (i.e., composed of cases with different slice thicknesses). These methods are (1) aggregate training using the entire suite of data at their native thickness, (2) homogeneous subset training that uses only the subset of training data that matches each testing case, and (3) resampling all training and testing cases to a common thickness. We believe this study has important implications for how CT is acquired, processed, and stored. We make use of 192 CT cases acquired at a thickness of 1.25 mm and 283 cases at 2.5 mm. These data are from the publicly available Lung Nodule Analysis 2016 dataset. In our study, CAD performance at 2.5 mm is comparable with that at 1.25 mm and is much better than at higher thicknesses. Also, resampling all training and testing cases to 2.5 mm provides the best performance among the three training methods compared in terms of accuracy, memory consumption, and computational time

Computer-aided detection of lung nodules: A review

We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas

Computerâ aided detection of lung nodules: False positive reduction using a 3D gradient field method and 3D ellipsoid fitting

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134878/1/mp4667.pd

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Classificação de nódulos pulmonares baseada em redes neurais convolucionais profundas em radiografias

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O câncer de pulmão, que se caracteriza pela presença de nódulos, é o tipo mais comum de câncer em todo o mundo, além de ser um dos mais agressivos e fatais, com 20% da mortalidade total por câncer. A triagem do câncer de pulmão pode ser realizada por radiologistas que analisam imagens de raios-X de tórax (CXR). No entanto, a detecção de nódulos pulmonares é uma tarefa difícil devido a sua grande variabilidade, limitações humanas de memória, distração e fadiga, entre outros fatores. Essas dificuldades motivam o desenvolvimento de sistemas de diagnóstico por computador (CAD) para apoiar radiologistas na detecção de nódulos pulmonares. A classificação do nódulo do pulmão é um dos principais tópicos relacionados aos sistemas de CAD. Embora as redes neurais convolucionais (CNN) tenham demonstrado um bom desempenho em muitas tarefas, há poucas explorações de seu uso para classificar nódulos pulmonares em imagens CXR. Neste trabalho, propusemos e analisamos um arcabouço para a detecção de nódulos pulmonares em imagens de CXR que inclui segmentação da área pulmonar, localização de nódulos e classificação de nódulos candidatos. Apresentamos um método para classificação de nódulos candidatos com CNNs treinadas a partir do zero. A eficácia do nosso método baseia-se na seleção de parâmetros de aumento de dados, no projeto de uma arquitetura CNN especializada, no uso da regularização de dropout na rede, inclusive em camadas convolucionais, e no tratamento da falta de amostras de nódulos em comparação com amostras de fundo, balanceando mini-lotes em cada iteração da descida do gradiente estocástico. Todas as decisões de seleção do modelo foram tomadas usando-se um subconjunto de imagens CXR da base Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) separadamente. Então, utilizamos todas as imagens com nódulos no conjunto de dados da Japanese Society of Radiological Technology (JSRT) para avaliação. Nossos experimentos mostraram que as CNNs foram capazes de alcançar resultados competitivos quando comparados com métodos da literatura. Nossa proposta obteve uma curva de operação (AUC) de 7.51 considerando 10 falsos positivos por imagem (FPPI) e uma sensibilidade de 71.4% e 81.0% com 2 e 5 FPPI, respectivamenteAbstract: Lung cancer, which is characterized by the presence of nodules, is the most common type of cancer around the world, as well as one of the most aggressive and deadliest cancer, with 20% of total cancer mortality. Lung cancer screening can be performed by radiologists analyzing chest X-ray (CXR) images. However, the detection of lung nodules is a difficult task due to their wide variability, human limitations of memory, distraction and fatigue, among other factors. These difficulties motivate the development of computer-aided diagnosis (CAD) systems for supporting radiologists in detecting lung nodules. Lung nodule classification is one of the main topics related to CAD systems. Although convolutional neural networks (CNN) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in CXR images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini-batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology (JSRT) dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state-of-the-art methods. Our proposal obtained an area under the free-response receiver operating characteristic (AUC) curve of 7.51 considering 10 false positives per image (FPPI), and a sensitivity of 71.4% and 81.0% with 2 and 5 FPPI, respectivelyMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

A computerized scheme for lung nodule detection in multiprojection chest radiography: Lung nodule detection in multiprojection chest radiography

Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists’ performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme

Massive training artificial immune recognition system for lung nodules detection

In the early detection and diagnosis of lung nodule, computer aided detection (CAD) has become crucial to assist radiologists in interpreting medical images and decision making. However, some limitations have been found in the existing CAD algorithms for detecting lung nodules, such as imprecision classification due to inaccurate segmentation and lengthy computation time. In this research, Massive Training Artificial Immune Recognition System (MTAIRS) is proposed to detect lung nodules on Computed Tomography (CT) scans. MTAIRS is developed based on the pixel machine learning and artificial immune-based system-Artificial Immune Recognition System (AIRS). Two versions of proposed algorithms have been investigated in the study: MTAIRS 1 and MTAIRS 2. Since segmentation and feature calculation are not implemented in the pixel-based machine learning, the loss of information can be avoided during the data training in MTAIRS 1 and MTAIRS 2. The experiment and analysis find that MTAIRS 1 and MTAIRS 2 have successfully reduced the computation time and accomplished good accuracy in the detection of lung nodules on CT scans compared to other well-known pixel-based classification algorithms. Furthermore, MTAIRS 1 and MTAIRS 2 are investigated to improve their performance in eliminating the false positives. A weighted non-linear affinity function is employed in the training of MTAIRS 1 and MTAIRS 2 to replace Euclidean distance in affinity measurement. The enhanced algorithms named, E-MTAIRS 1 and E-MTAIRS 2 are capable to reduce the false positives in the non-nodule classification while maintaining the accuracy in nodule detection. In order to further provide comparative analysis of pixel-based classification algorithms in lung nodules detection, a pixel-based evaluation method of Kullback Leibler (KL) divergence is proposed in this study. Based on the pixel-based quantitative analysis, MTAIRS 1 performs better in the elimination of false positives, while MTAIRS 2 in lung nodules detection. The average detection accuracy for both MTAIRS algorithms is 95%

Lung nodule modeling and detection for computerized image analysis of low dose CT imaging of the chest.

From a computerized image analysis prospective, early diagnosis of lung cancer involves detection of doubtful nodules and classification into different pathologies. The detection stage involves a detection approach, usually by template matching, and an authentication step to reduce false positives, usually conducted by a classifier of one form or another; statistical, fuzzy logic, support vector machines approaches have been tried. The classification stage matches, according to a particular approach, the characteristics (e.g., shape, texture and spatial distribution) of the detected nodules to common characteristics (again, shape, texture and spatial distribution) of nodules with known pathologies (confirmed by biopsies). This thesis focuses on the first step; i.e., nodule detection. Specifically, the thesis addresses three issues: a) understanding the CT data of typical low dose CT (LDCT) scanning of the chest, and devising an image processing approach to reduce the inherent artifacts in the scans; b) devising an image segmentation approach to isolate the lung tissues from the rest of the chest and thoracic regions in the CT scans; and c) devising a nodule modeling methodology to enhance the detection rate and lend benefits for the ultimate step in computerized image analysis of LDCT of the lungs, namely associating a pathology to the detected nodule. The methodology for reducing the noise artifacts is based on noise analysis and examination of typical LDCT scans that may be gathered on a repetitive fashion; since, a reduction in the resolution is inevitable to avoid excessive radiation. Two optimal filtering methods are tested on samples of the ELCAP screening data; the Weiner and the Anisotropic Diffusion Filters. Preference is given to the Anisotropic Diffusion Filter, which can be implemented on 7x7 blocks/windows of the CT data. The methodology for lung segmentation is based on the inherent characteristics of the LDCT scans, shown as distinct bi-modal gray scale histogram. A linear model is used to describe the histogram (the joint probability density function of the lungs and non-lungs tissues) by a linear combination of weighted kernels. The Gaussian kernels were chosen, and the classic Expectation-Maximization (EM) algorithm was employed to estimate the marginal probability densities of the lungs and non-lungs tissues, and select an optimal segmentation threshold. The segmentation is further enhanced using standard shape analysis based on mathematical morphology, which improves the continuity of the outer and inner borders of the lung tissues. This approach (a preliminary version of it appeared in [14]) is found to be adequate for lung segmentation as compared to more sophisticated approaches developed at the CVIP Lab (e.g., [15][16]) and elsewhere. The methodology developed for nodule modeling is based on understanding the physical characteristics of the nodules in LDCT scans, as identified by human experts. An empirical model is introduced for the probability density of the image intensity (or Hounsfield units) versus the radial distance measured from the centroid – center of mass - of typical nodules. This probability density showed that the nodule spatial support is within a circle/square of size 10 pixels; i.e., limited to 5 mm in length; which is within the range that the radiologist specify to be of concern. This probability density is used to fill in the intensity (or Hounsfield units) of parametric nodule models. For these models (e.g., circles or semi-circles), given a certain radius, we calculate the intensity (or Hounsfield units) using an exponential expression for the radial distance with parameters specified from the histogram of an ensemble of typical nodules. This work is similar in spirit to the earlier work of Farag et al., 2004 and 2005 [18][19], except that the empirical density of the radial distance and the histogram of typical nodules provide a data-driven guide for estimating the intensity (or Hounsfield units) of the nodule models. We examined the sensitivity and specificity of parametric nodules in a template-matching framework for nodule detection. We show that false positives are inevitable problems with typical machine learning methods of automatic lung nodule detection, which invites further efforts and perhaps fresh thinking into automatic nodule detection. A new approach for nodule modeling is introduced in Chapter 5 of this thesis, which brings high promise in both the detection, and the classification of nodules. Using the ELCAP study, we created an ensemble of four types of nodules and generated a nodule model for each type based on optimal data reduction methods. The resulting nodule model, for each type, has lead to drastic improvements in the sensitivity and specificity of nodule detection. This approach may be used as well for classification. In conclusion, the methodologies in this thesis are based on understanding the LDCT scans and what is to be expected in terms of image quality. Noise reduction and image segmentation are standard. The thesis illustrates that proper nodule models are possible and indeed a computerized approach for image analysis to detect and classify lung nodules is feasible. Extensions to the results in this thesis are immediate and the CVIP Lab has devised plans to pursue subsequent steps using clinical data

Computer-aided diagnosis in chest radiography

Chest radiographs account for more than half of all radiological examinations; the chest is the mirror of health and disease. This thesis is about techniques for computer analysis of chest radiographs. It describes methods for texture analysis and segmenting the lung fields and rib cage in a chest film. It includes a description of an automatic system for detecting regions with abnormal texture, that is applied to a database of images from a tuberculosis screening program