Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography

Two-Stage Hybrid Approach of Deep Learning Networks for Interstitial Lung Disease Classification

High-resolution computed tomography (HRCT) images in interstitial lung disease (ILD) screening can help improve healthcare quality. However, most of the earlier ILD classification work involves time-consuming manual identification of the region of interest (ROI) from the lung HRCT image before applying the deep learning classification algorithm. This paper has developed a two-stage hybrid approach of deep learning networks for ILD classification. A conditional generative adversarial network (c-GAN) has segmented the lung part from the HRCT images at the first stage. The c-GAN with multiscale feature extraction module has been used for accurate lung segmentation from the HRCT images with lung abnormalities. At the second stage, a pretrained ResNet50 has been used to extract the features from the segmented lung image for classification into six ILD classes using the support vector machine classifier. The proposed two-stage algorithm takes a whole HRCT as input eliminating the need for extracting the ROI and classifies the given HRCT image into an ILD class. The performance of the proposed two-stage deep learning network-based ILD classifier has improved considerably due to the stage-wise improvement of deep learning algorithm performance.

[Diagnostics of the presence of once of death and peculiarities of performance of the forensic medical examination in post-mortal facial condition]

The purpose of the research is to summarize the current information about post-mortem interval (PMI) estimating and the peculiarities of the forensic medical examination in postmortem glaciation of the corpse. On the territory of most regions of the Russian Federation, the autumn-winter period passes with a significant decrease in ambient temperature, which makes this topic relevant. The article describes in detail the mechanisms of freezing of human cells and tissues, methods for diagnosing PMI, the peculiarities of the examination in postmortem glaciation, and also put forward directions for further research.

Texture-based classification of lung disease patterns in chronic hypersensitivity pneumonitis and comparison to clinical outcomes

Computer-aided detection algorithms applied to CT lung imaging have the potential to objectively quantify pulmonary pathology. We aim to develop an automatic classification method based on textural features able to classify healthy and pathological patterns on CT lung images and to quantify the extent of each disease pattern in a group of patients with chronic hypersensitivity pneumonitis (cHP), in comparison to pulmonary function tests (PFTs).27 cHP patients were scanned via high resolution CT (HRCT) at full-inspiration. Regions of interest (ROIs) were extracted and labeled as normal (NOR), ground glass opacity (GGO), reticulation (RET), consolidation (C), honeycombing (HB) and air trapping (AT). For each ROI, statistical, morphological and fractal parameters were computed. For automatic classification, we compared two classification methods (Bayesian and Support Vector Machine) and three ROI sizes. The classifier was therefore applied to the overall CT images and the extent of each class was calculated and compared to PFTs. Better classification accuracy was found for the Bayesian classifier and the 16x16 ROI size: 92.1±2.7%. The extent of GGO, HB and NOR significantly correlated with forced vital capacity (FVC) and the extent of NOR with carbon monoxide diffusing capacity (DLCO).Clinical Relevance- Texture analysis can differentiate and objectively quantify pathological classes in the lung parenchyma and may represent a quantitative diagnostic tool in cHP.

Optimal Deep Belief Network with Opposition based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach

BACKGROUND AND OBJECTIVE

This research proposes a successful method of extracting Gray-Level Co-occurrence Matrix (GLCM) picture handling models to classify low-and high-metastatic cancer organisms with five prevalent cancer cell line pairs, coupled with the scanning laser picture projection technique and the typical textural function, i.e. contrast, correlation, power, temperature and homogeneity. The most significant level of disease for highly metastatic cancer cells are the degree of disturbance, contrast as well as entropy refers to the energy and homogeneity. A texture classification scheme to quantify the emphysema in Computed Tomography (CT) pictures is performed. Local binary models (LBP) are used to characterize areas of concern as texture characteristics and intensity histograms. A wavelet filter is used to acquire the informative matrix of each picture and decrease the dimensionality of the function space in the suggested method. A four-layer profound creed network is also used to obtain characteristics of elevated stage. Local Tangent Space Alignment (LTSA) is then used to compress the multi-domain defect characteristics into low dimensional vectors as a dimension reduction method. An unmonitored deep-belief network (DBN) is intended for the second phase to learn the unmarked characteristics. The strategy suggested uses Opposition Based Teaching (OBL), Position Clamping (PC) and the Cauchy Mutation (CM) to improve the fundamental PBA efficiency.

METHODS

This research presents a fresh meta-heuristic algorithm called Opposition-Based Pity Beetle Algorithm (OPBA), which assesses effectiveness against state-of-the-art algorithms. OBL speeds up the convergence of the technique as both PC and CM assist OPBA with escaping local optima. The suggested algorithm was motivated by the behaviour of the beetle, which had been named six-toothed spruce bark beetle to aggregate nests and meals. This beetle can be found and harvested from weakened trees ' bark in a forest, while its populace can also infest healthy and robust trees when it exceeds the specified threshold.

RESULTS & CONCLUSION

The methodology has been evaluated on CT imagery from the Lung Image Database Consortium and Image Resources Initiative (LIDC-IDRI), with a maximum sensitivity of 96.86%, precision of 97.24%, and an accuracy of 97.92%.

Meta-analysis of the radiological and clinical features of Usual Interstitial Pneumonia (UIP) and Nonspecific Interstitial Pneumonia (NSIP)

PURPOSE

To conduct a meta-analysis to determine specific computed tomography (CT) patterns and clinical features that discriminate between nonspecific interstitial pneumonia (NSIP) and usual interstitial pneumonia (UIP).

MATERIALS AND METHODS

The PubMed/Medline and Embase databases were searched for studies describing the radiological patterns of UIP and NSIP in chest CT images. Only studies involving histologically confirmed diagnoses and a consensus diagnosis by an interstitial lung disease (ILD) board were included in this analysis. The radiological patterns and patient demographics were extracted from suitable articles. We used random-effects meta-analysis by DerSimonian & Laird and calculated pooled odds ratios for binary data and pooled mean differences for continuous data.

RESULTS

Of the 794 search results, 33 articles describing 2,318 patients met the inclusion criteria. Twelve of these studies included both NSIP (338 patients) and UIP (447 patients). NSIP-patients were significantly younger (NSIP: median age 54.8 years, UIP: 59.7 years; mean difference (MD) -4.4; p = 0.001; 95% CI: -6.97 to -1.77), less often male (NSIP: median 52.8%, UIP: 73.6%; pooled odds ratio (OR) 0.32; p<0.001; 95% CI: 0.17 to 0.60), and less often smokers (NSIP: median 55.1%, UIP: 73.9%; OR 0.42; p = 0.005; 95% CI: 0.23 to 0.77) than patients with UIP. The CT findings from patients with NSIP revealed significantly lower levels of the honeycombing pattern (NSIP: median 28.9%, UIP: 73.4%; OR 0.07; p<0.001; 95% CI: 0.02 to 0.30) with less peripheral predominance (NSIP: median 41.8%, UIP: 83.3%; OR 0.21; p<0.001; 95% CI: 0.11 to 0.38) and more subpleural sparing (NSIP: median 40.7%, UIP: 4.3%; OR 16.3; p = 0.005; 95% CI: 2.28 to 117).

CONCLUSION

Honeycombing with a peripheral predominance was significantly associated with a diagnosis of UIP. The NSIP pattern showed more subpleural sparing. The UIP pattern was predominantly observed in elderly males with a history of smoking, whereas NSIP occurred in a younger patient population.

Pulmonary Textures Classification via a Multi-Scale Attention Network

Precise classification of pulmonary textures is crucial to develop a computer aided diagnosis (CAD) system of diffuse lung diseases (DLDs). Although deep learning techniques have been applied to this task, the classification performance is not satisfied for clinical requirements, since commonly-used deep networks built by stacking convolutional blocks are not able to learn discriminative feature representation to distinguish complex pulmonary textures. For addressing this problem, we design a multi-scale attention network (MSAN) architecture comprised by several stacked residual attention modules followed by a multi-scale fusion module. Our deep network can not only exploit powerful information on different scales but also automatically select optimal features for more discriminative feature representation. Besides, we develop visualization techniques to make the proposed deep model transparent for humans. The proposed method is evaluated by using a large dataset. Experimental results show that our method has achieved the average classification accuracy of 94.78% and the average f-value of 0.9475 in the classification of 7 categories of pulmonary textures. Besides, visualization results intuitively explain the working behavior of the deep network. The proposed method has achieved the state-of-the-art performance to classify pulmonary textures on high resolution CT images.

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this papet, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D convolutional neural network and transfer learning. Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task-dependent feature representations into a CAD system via a graph-regularized sparse multi-task learning framework. In the second approach, we explore an unsupervised learning algorithm to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion approaches in computer vision, we propose to use proportion-support vector machine for characterizing tumors. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.

Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification

Existing deep-learning-based pulmonary nodule classification models usually use images and benign-malignant labels as inputs for training. Image attributes of the nodules, as human-nameable high-level semantic labels, are rarely used to build a convolutional neural network (CNN). In this paper, a new method is proposed to combine the advantages of two classifications, which are pulmonary nodule benign-malignant classification and pulmonary nodule image attributes classification, into a deep learning network to improve the accuracy of pulmonary nodule classification. For this purpose, a unique 3D CNN is built to learn image attribute and benign-malignant classification simultaneously. A novel loss function is designed to balance the influence of two different kinds of classifications. The CNN is trained by a publicly available lung image database consortium (LIDC) dataset and is tested by a cross-validation method to predict the risk of a pulmonary nodule being malignant. This proposed method generates the accuracy of 91.47%, which is better than many existing models. Experimental findings show that if the CNN is built properly, the nodule attributes classification and benign-malignant classification can benefit from each other. By using nodule attribute learning as a control factor in a deep learning scheme, the accuracy of pulmonary nodule classification can be significantly improved by using a deep learning scheme.

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

The purpose of this study was to investigate a preclinical spectral photon-counting CT (SPCCT) prototype compared to conventional CT for pulmonary imaging. A custom-made lung phantom, including nodules of different sizes and shapes, was scanned with a preclinical SPCCT and a conventional CT in standard and high-resolution (HR-CT) mode. Volume estimation was evaluated by linear regression. Shape similarity was evaluated with the Dice similarity coefficient. Spatial resolution was investigated via MTF for each imaging system. In-vivo rabbit lung images from the SPCCT system were subjectively reviewed. Evaluating the volume estimation, linear regression showed best results for the SPCCT compared to CT and HR-CT with a root mean squared error of 21.3 mm³, 28.5 mm³ and 26.4 mm³ for SPCCT, CT and HR-CT, respectively. The Dice similarity coefficient was superior for SPCCT throughout nodule shapes and all nodule sizes (mean, SPCCT: 0.90; CT: 0.85; HR-CT: 0.85). 10% MTF improved from 10.1 LP/cm for HR-CT to 21.7 LP/cm for SPCCT. Visual investigation of small pulmonary structures was superior for SPCCT in the animal study. In conclusion, the SPCCT prototype has the potential to improve the assessment of lung structures due to higher resolution compared to conventional CT.

3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification

Lung cancer mortality is currently the highest among all kinds of fatal cancers. With the help of computer-aided detection systems, a timely detection of malignant pulmonary nodule at early stage could improve the patient survival rate efficiently. However, the sizes of the pulmonary nodules are usually various, and it is more difficult to detect small diameter nodules. The traditional convolution neural network uses pooling layers to reduce the resolution progressively, but it hampers the network’s ability to capture the tiny but vital features of the pulmonary nodules. To tackle this problem, we propose a novel 3D spatial pyramid dilated convolution network to classify the malignancy of the pulmonary nodules. Instead of using the pooling layers, we use 3D dilated convolution to learn the detailed characteristic information of the pulmonary nodules. Furthermore, we show that the fusion of multiple receptive fields from different dilated convolutions could further improve the classification performance of the model. Extensive experimental results demonstrate that our model achieves a better result with an accuracy of 88 . 6 % , which outperforms other state-of-the- art methods.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

Advances in Imaging and Automated Quantification of Pulmonary Diseases in Non-neoplastic Diseases

Histological examination has always been the gold standard for the detection and quantification of lung remodeling. However, this method has some limitations regarding the invasiveness of tissue acquisition. Quantitative imaging methods enable the acquisition of valuable information on lung structure and function without the removal of tissue from the body; thus, they are useful for disease identification and follow-up. This article reviews the various quantitative imaging modalities used currently for the non-invasive study of chronic obstructive pulmonary disease, asthma, and interstitial lung diseases. Some promising computer-aided diagnosis methods are also described.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 × 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance ( ~ 85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Freezing effect on brain density in postmortem CT

Two 60-year-old males were found at their homes whose bodies had deteriorated due to putrefaction. To prevent worm invasion and minimize deterioration, dry ice was used prior to the autopsy investigation. Prior to autopsy, postmortem CT demonstrated a decreased density in brain parenchyma at the dry-iced side, and autopsy revealed deteriorated brain parenchyma with frozen effect (presented like sherbet). Moreover, the deteriorated cerebral parenchyma maintained their structure and they were evaluated by cutting. When lower CT density presents in postmortem CT, the freezing effect may need to be considered and the physician should evaluate the cadaver's postmortem condition to prevent misdiagnoses.

Classification of interstitial lung disease patterns using local DCT features and random forest

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Determination of lung regions on chest ct images with diffuse lung diseases by use of anatomical structures and pulmonary textures

To analyze diffuse lung diseases based on chest region computed tomography (CT) imaging by using a computer-aided diagnosis (CAD) system, it is necessary to first determine the lung regions subject to analysis. The lung regions can be selected relatively easily for healthy individuals, by applying a threshold. Selecting an area by using a threshold-based method can be difficult when dealing with lungs with diffuse lung diseases, owing to the abnormal opacities that characterize the diseases. Trials for determining the lung regions were conducted in this study, through texture analysis and machine learning, by narrowing down the lung regions to rough regions, and by referring to ribs and the diaphragm. This method can be used for determining lung regions for analysis of diffuse lung diseases.

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

A sparse representation based method to classify pulmonary patterns of diffuse lung diseases

We applied and optimized the sparse representation (SR) approaches in the computer-aided diagnosis (CAD) to classify normal tissues and five kinds of diffuse lung disease (DLD) patterns: consolidation, ground-glass opacity, honeycombing, emphysema, and nodule. By using the K-SVD which is based on the singular value decomposition (SVD) and orthogonal matching pursuit (OMP), it can achieve a satisfied recognition rate, but too much time was spent in the experiment. To reduce the runtime of the method, the K-Means algorithm was substituted for the K-SVD, and the OMP was simplified by searching the desired atoms at one time (OMP1). We proposed three SR based methods for evaluation: SR1 (K-SVD+OMP), SR2 (K-Means+OMP), and SR3 (K-Means+OMP1). 1161 volumes of interest (VOIs) were used to optimize the parameters and train each method, and 1049 VOIs were adopted to evaluate the performances of the methods. The SR based methods were powerful to recognize the DLD patterns (SR1: 96.1%, SR2: 95.6%, SR3: 96.4%) and significantly better than the baseline methods. Furthermore, when the K-Means and OMP1 were applied, the runtime of the SR based methods can be reduced by 98.2% and 55.2%, respectively. Therefore, we thought that the method using the K-Means and OMP1 (SR3) was efficient for the CAD of the DLDs.

Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development

PURPOSE

To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP).

METHODS AND MATERIALS

A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identified in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction.

RESULTS

For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84).

CONCLUSIONS

A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.

Automated 3D ιnterstitial lung disease εxtent quantification: performance evaluation and correlation to PFTs

In this study, the performance of a recently proposed computer-aided diagnosis (CAD) scheme in detection and 3D quantification of reticular and ground glass pattern extent in chest computed tomography of interstitial lung disease (ILD) patients is evaluated. CAD scheme performance was evaluated on a dataset of 37 volumetric chest scans, considering five representative axial anatomical levels per scan. CAD scheme reliability analysis was performed by estimating agreement (intraclass correlation coefficient, ICC) of automatically derived ILD pattern extent to semi-quantitative disease extent assessment in terms of 29-point rating scale provided by two expert radiologists. Receiver operating characteristic (ROC) analysis was employed to assess CAD scheme accuracy in ILD pattern detection in terms of area under ROC curve (A z ). Correlation of reticular and ground glass volumetric pattern extent to pulmonary function tests (PFTs) was also investigated. CAD scheme reliability was substantial for ILD extent (ICC = 0.809) and distinct reticular pattern extent (0.806) and moderate for distinct ground glass pattern extent (0.543), performing within inter-observer agreement. CAD scheme demonstrated high accuracy in detecting total ILD (A z  = 0.950 ± 0.018), while accuracy in detecting distinct reticular and ground glass patterns was 0.920 ± 0.023 and 0.883 ± 0.024, respectively. Moderate and statistically significant negative correlation was found between reticular volumetric pattern extent and diffusing capacity, forced expiratory volume in 1 s, forced vital capacity, and total lung capacity (R = -0.581, -0.513, -0.494, and -0.446, respectively), similar to correlations found between radiologists' semi-quantitative ratings with PFTs. CAD-based quantification of disease extent is in agreement with radiologists' semi-quantitative assessment and correlates to specific PFTs, suggesting a potential imaging biomarker for ILD staging and management.

Ant colony optimization approaches to clustering of lung nodules from CT images

Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

Mycobacterium tuberculosis dysregulates MMP/TIMP balance to drive rapid cavitation and unrestrained bacterial proliferation

Active tuberculosis (TB) often presents with advanced pulmonary disease, including irreversible lung damage and cavities. Cavitary pathology contributes to antibiotic failure, transmission, morbidity and mortality. Matrix metalloproteinases (MMPs), in particular MMP-1, are implicated in TB pathogenesis. We explored the mechanisms relating MMP/TIMP imbalance to cavity formation in a modified rabbit model of cavitary TB. Our model resulted in consistent progression of consolidation to human-like cavities (100% by day 28), with resultant bacillary burdens (>10(7) CFU/g) far greater than those found in matched granulomatous tissue (10(5) CFU/g). Using a novel, breath-hold computed tomography (CT) scanning and image analysis protocol, we showed that cavities developed rapidly from areas of densely consolidated tissue. Radiological change correlated with a decrease in functional lung tissue, as estimated by changes in lung density during controlled pulmonary expansion (R(2)  = 0.6356, p < 0.0001). We demonstrated that the expression of interstitial collagenase (MMP-1) was specifically greater in cavitary compared to granulomatous lesions (p < 0.01), and that TIMP-3 significantly decreased at the cavity surface. Our findings demonstrated that an MMP-1/TIMP imbalance is associated with the progression of consolidated regions to cavities containing very high bacterial burdens. Our model provided mechanistic insight, correlating with human disease at the pathological, microbiological and molecular levels. It also provided a strategy to investigate therapeutics in the context of complex TB pathology. We used these findings to predict a MMP/TIMP balance in active TB and confirmed this in human plasma, revealing the potential of MMP/TIMP levels as key components of a diagnostic matrix aimed at distinguishing active from latent TB (PPV = 92.9%, 95% CI 66.1-99.8%, NPV = 85.6%; 95% CI 77.0-91.9%).

Lung nodule classification with multilevel patch-based context analysis

In this paper, we propose a novel classification method for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed method is based on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive patch-based division is used to construct concentric multilevel partition; then, a new feature set is designed to incorporate intensity, texture, and gradient information for image patch feature description, and then a contextual latent semantic analysis-based classifier is designed to calculate the probabilistic estimations for the relevant images. Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance.

Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy

This study examines the correlation between the radiologist-defined severity of normal tissue damage following radiation therapy (RT) for lung cancer treatment and a set of mathematical descriptors of computed tomography (CT) scan texture ('texture features'). A pre-therapy CT scan and a post-therapy CT scan were retrospectively collected under IRB approval for each of the 25 patients who underwent definitive RT (median dose: 66 Gy). Sixty regions of interest (ROIs) were automatically identified in the non-cancerous lung tissue of each post-therapy scan. A radiologist compared post-therapy scan ROIs with pre-therapy scans and categorized each as containing no abnormality, mild abnormality, moderate abnormality, or severe abnormality. Twenty texture features that characterize gray-level intensity, region morphology, and gray-level distribution were calculated in post-therapy scan ROIs and compared with anatomically matched ROIs in the pre-therapy scan. Linear regression and receiver operating characteristic (ROC) analysis were used to compare the percent feature value change (ΔFV) between ROIs at each category of visible radiation damage. Most ROIs contained no (65%) or mild abnormality (30%). ROIs with moderate (3%) or severe (2%) abnormalities were observed in 9 patients. For 19 of 20 features, ΔFV was significantly different among severity levels. For 12 features, significant differences were observed at every level. Compared with regions with no abnormalities, ΔFV for these 12 features increased, on average, by 1.5%, 12%, and 30%, respectively, for mild, moderate, and severe abnormalitites. Area under the ROC curve was largest when comparing ΔFV in the highest severity level with the remaining three categories (mean AUC across features: 0.84). In conclusion, 19 features that characterized the severity of radiologic changes from pre-therapy scans were identified. These features may be used in future studies to quantify acute normal lung tissue damage following RT.

Detection of time-varying structures by large deformation diffeomorphic metric mapping to aid reading of high-resolution CT images of the lung

Objectives

To evaluate the accuracy of advanced non-linear registration of serial lung Computed Tomography (CT) images using Large Deformation Diffeomorphic Metric Mapping (LDDMM).

Methods

Fifteen cases of lung cancer with serial lung CT images (interval: 62.2±26.9 days) were used. After affine transformation, three dimensional, non-linear volume registration was conducted using LDDMM with or without cascading elasticity control. Registration accuracy was evaluated by measuring the displacement of landmarks placed on vessel bifurcations for each lung segment. Subtraction images and Jacobian color maps, calculated from the transformation matrix derived from image warping, were generated, which were used to evaluate time-course changes of the tumors.

Results

The average displacement of landmarks was 0.02±0.16 mm and 0.12±0.60 mm for proximal and distal landmarks after LDDMM transformation with cascading elasticity control, which was significantly smaller than 3.11±2.47 mm and 3.99±3.05 mm, respectively, after affine transformation. Emerged or vanished nodules were visualized on subtraction images, and enlarging or shrinking nodules were displayed on Jacobian maps enabled by highly accurate registration of the nodules using LDDMM. However, some residual misalignments were observed, even with non-linear transformation when substantial changes existed between the image pairs.

Conclusions

LDDMM provides accurate registration of serial lung CT images, and temporal subtraction images with Jacobian maps help radiologists to find changes in pulmonary nodules.

Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods

We have been developing a computer-aided detection (CAD) scheme for pneumoconiosis based on a rule-based plus artificial neural network (ANN) analysis of power spectra. In this study, we have developed three enhancement methods for the abnormal patterns to reduce false-positive and false-negative values. The image database consisted of 2 normal and 15 abnormal chest radiographs. The International Labour Organization standard chest radiographs with pneumoconiosis were categorized as subcategory, size, and shape of pneumoconiosis. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from normal and abnormal lungs. Three new enhanced methods were obtained by window function, top-hat transformation, and gray-level co-occurrence matrix analysis. We calculated the power spectrum (PS) of all ROIs by Fourier transform. For the classification between normal and abnormal ROIs, we applied a combined analysis using the ruled-based plus the ANN method. To evaluate the overall performance of this CAD scheme, we employed ROC analysis for distinguishing between normal and abnormal ROIs. On the chest radiographs of the highest categories (severe pneumoconiosis) and the lowest categories (early pneumoconiosis), this CAD scheme achieved area under the curve (AUC) values of 0.93 ± 0.02 and 0.72 ± 0.03. The combined rule-based plus ANN method with the three new enhanced methods obtained the highest classification performance for distinguishing between abnormal and normal ROIs. Our CAD system based on the three new enhanced methods would be useful in assisting radiologists in the classification of pneumoconiosis.

Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions

PURPOSE

The aim of this study was to compare three demons registration-based methods to identify spatially matched regions in serial computed tomography (CT) scans for use in texture analysis.

METHODS

Two thoracic CT scans containing no lung abnormalities and acquired during serial examinations separated by at least one week were retrospectively collected from 27 patients. Over 1000 regions of interest (ROIs) were randomly placed in the lungs of each baseline scan. Anatomically matched ROIs in the corresponding follow-up scan were placed by mapping the baseline scan ROI center pixel to (1) the original follow-up scan, (2) the follow-up scan resampled to match the baseline scan voxel size, and (3) the follow-up scan aligned to the baseline scan through affine registration. Mappings used the vector field obtained through demons deformable registration of each follow-up scan variant to the baseline scan. 140 texture features distributed among five feature classes were calculated in all ROIs. Feature value differences between paired ROIs were evaluated using Bland-Altman 95% limits of agreement. For each feature, (1) the mean feature value change and (2) the difference between the upper and lower limits of agreement were normalized to the mean feature value to obtain, respectively, the normalized bias and normalized range of agreement (nRoA). Nonparametric tests were used to evaluate differences in normalized bias and nRoA across the three methods.

RESULTS

Because patient CT scans contained no pathology, minimal changes in feature values were expected (i.e., low nRoA and normalized bias). Seventy-five features with very large feature value variability (nRoA ≥ 100%) were excluded from further analysis. Across the remaining 65 features, significant differences in normalized bias were observed among the three methods. The lowest normalized bias (median: 0.06%) was achieved when feature values were calculated on original follow-up scans. The affine registration method achieved the lowest nRoA, though nRoA was not significantly increased using original follow-up scans. Features with low nRoA values also had low normalized bias, though the converse was not necessarily true. Using nRoA as a metric, a set of 20 features having both low nRoA and normalized bias were identified.

CONCLUSIONS

Three methods to facilitate texture analysis of serial CT scans using demons registration for ROI placement were evaluated. The bias in feature value change between matched ROIs was minimized when feature values were calculated on original baseline and follow-up scans. A set of features that had both low bias and variability (nRoA) in feature value change using this method were identified. This texture analysis approach could facilitate future measurement of pathologic changes between CT scans without necessitating calculation of feature values on deformed scans.

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: comparison to a Bayesian classifier

PURPOSE

To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data.

METHODS

Two experienced radiologists marked sets of 600 rectangular 20 × 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions.

RESULTS

For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI data obtained from both scanners, the classification accuracies with the SVM and Bayesian classifiers were 92% and 77%, respectively. The selected features resulting from the classification process differed by scanner, with more features included for the classification of the integrated HRCT data than for the classification of the HRCT data from each scanner. For the integrated data, consisting of HRCT images of both scanners, the classification accuracy based on the SVM was statistically similar to the accuracy of the data obtained from each scanner. However, the classification accuracy of the integrated data using the Bayesian classifier was significantly lower than the classification accuracy of the ROI data of each scanner.

CONCLUSIONS

The use of an integrated dataset along with a SVM classifier rather than a Bayesian classifier has benefits in terms of the classification accuracy of HRCT images acquired with more than one scanner. This finding is of relevance in studies involving large number of images, as is the case in a multicenter trial with different scanners.

Classification of diffuse lung diseases patterns by a sparse representation based method on HRCT images

This paper describes a computer-aided diagnosis (CAD) method to classify diffuse lung diseases (DLD) patterns on HRCT images. Due to the high variety and complexity of DLD patterns, the performance of conventional methods on recognizing DLD patterns featured by geometrical information is limited. In this paper, we introduced a sparse representation based method to classify normal tissues and five types of DLD patterns including consolidation, ground-glass opacity, honeycombing, emphysema and nodular. Both CT values and eigenvalues of Hessian matrices were adopted to calculate local features. The 2360 VOIs from 117 subjects were separated into two independent set. One set was used to optimize parameters, and the other set was adopted to evaluation. The proposed technique has a overall accuracy of 95.4%. Experimental results show that our method would be useful to classify DLD patterns on HRCT images.

Quantitative computed tomography imaging of interstitial lung diseases

PURPOSE

High-resolution chest computed tomography (HRCT) is essential in the characterization of interstitial lung disease. The HRCT features of some diseases can be diagnostic. Longitudinal monitoring with HRCT can assess progression of interstitial lung disease; however, subtle changes in the volume and character of abnormalities can be difficult to assess. Accuracy of diagnosis can be dependent on expertise and experience of the radiologist, pathologist, or clinician. Quantitative analysis of thoracic HRCT has the potential to determine the extent of disease reproducibly, classify the types of abnormalities, and automate the diagnostic process.

MATERIALS AND METHODS

Novel software that utilizes histogram signatures to characterize pulmonary parenchyma was used to analyze chest HRCT data, including retrospective processing of clinical CT scans and research data from the Lung Tissue Research Consortium. Additional information including physiological, pathologic, and semiquantitative radiologist assessment was available to allow comparison of quantitative results, with visual estimates of the disease, physiological parameters, and measures of disease outcome.

RESULTS

Quantitative analysis results were provided in regional volumetric quantities for statistical analysis and a graphical representation. These results suggest that quantitative HRCT analysis can serve as a biomarker with physiological, pathologic, and prognostic significance.

CONCLUSIONS

It is likely that quantitative analysis of HRCT can be used in clinical practice as a means to aid in identifying a probable diagnosis, stratifying prognosis in early disease, and consistently determining progression of the disease or response to therapy. Further optimization of quantitative techniques and longitudinal analysis of well-characterized subjects would be helpful in validating these methods.

Feature-based image patch approximation for lung tissue classification

In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.

A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen

PURPOSE

To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.

RESULTS

High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).

CONCLUSIONS

Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors.

Lung texture in serial thoracic CT scans: assessment of change introduced by image registration

PURPOSE

The aim of this study was to quantify the effect of four image registration methods on lung texture features extracted from serial computed tomography (CT) scans obtained from healthy human subjects.

METHODS

Two chest CT scans acquired at different time points were collected retrospectively for each of 27 patients. Following automated lung segmentation, each follow-up CT scan was registered to the baseline scan using four algorithms: (1) rigid, (2) affine, (3) B-splines deformable, and (4) demons deformable. The registration accuracy for each scan pair was evaluated by measuring the Euclidean distance between 150 identified landmarks. On average, 1432 spatially matched 32 × 32-pixel region-of-interest (ROI) pairs were automatically extracted from each scan pair. First-order, fractal, Fourier, Laws' filter, and gray-level co-occurrence matrix texture features were calculated in each ROI, for a total of 140 features. Agreement between baseline and follow-up scan ROI feature values was assessed by Bland-Altman analysis for each feature; the range spanned by the 95% limits of agreement of feature value differences was calculated and normalized by the average feature value to obtain the normalized range of agreement (nRoA). Features with small nRoA were considered "registration-stable." The normalized bias for each feature was calculated from the feature value differences between baseline and follow-up scans averaged across all ROIs in every patient. Because patients had "normal" chest CT scans, minimal change in texture feature values between scan pairs was anticipated, with the expectation of small bias and narrow limits of agreement.

RESULTS

Registration with demons reduced the Euclidean distance between landmarks such that only 9% of landmarks were separated by ≥1 mm, compared with rigid (98%), affine (95%), and B-splines (90%). Ninety-nine of the 140 (71%) features analyzed yielded nRoA > 50% for all registration methods, indicating that the majority of feature values were perturbed following registration. Nineteen of the features (14%) had nRoA < 15% following demons registration, indicating relative feature value stability. Student's t-tests showed that the nRoA of these 19 features was significantly larger when rigid, affine, or B-splines registration methods were used compared with demons registration. Demons registration yielded greater normalized bias in feature value change than B-splines registration, though this difference was not significant (p = 0.15).

CONCLUSIONS

Demons registration provided higher spatial accuracy between matched anatomic landmarks in serial CT scans than rigid, affine, or B-splines algorithms. Texture feature changes calculated in healthy lung tissue from serial CT scans were smaller following demons registration compared with all other algorithms. Though registration altered the values of the majority of texture features, 19 features remained relatively stable after demons registration, indicating their potential for detecting pathologic change in serial CT scans. Combined use of accurate deformable registration using demons and texture analysis may allow for quantitative evaluation of local changes in lung tissue due to disease progression or treatment response.

Texture feature ranking with relevance learning to classify interstitial lung disease patterns

OBJECTIVE

The generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images.

METHODOLOGY

After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. 65 texture features were extracted from gray-level co-occurrence matrices (GLCMs). These features were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. The classification performance for different feature subsets was calculated for a k-nearest-neighbor (kNN) and a random forests classifier (RanForest), and support vector machines with a linear and a radial basis function kernel (SVMlin and SVMrbf).

RESULTS

For all classifiers, feature sets selected by the relevance ranking assessed by GMLVQ had a significantly better classification performance (p<0.05) for many texture feature sets compared to the MI approach. For kNN, RanForest, and SVMrbf, some of these feature subsets had a significantly better classification performance when compared to the set consisting of all features (p<0.05).

CONCLUSION

While this approach estimates the relevance of single features, future considerations of GMLVQ should include the pairwise correlation for the feature ranking, e.g. to reduce the redundancy of two equally relevant features.

Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms

Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.

Autologous non-myeloablative haemopoietic stem-cell transplantation compared with pulse cyclophosphamide once per month for systemic sclerosis (ASSIST): an open-label, randomised phase 2 trial

BACKGROUND

Non-randomised studies of haemopoietic stem-cell transplantation (HSCT) in systemic sclerosis have shown improvements in lung function and skin flexibility but high treatment-related mortality. We aimed to assess safety and efficacy of autologous non-myeloablative HSCT in a phase 2 trial compared with the standard of care, cyclophosphamide.

METHODS

In our open-label, randomised, controlled phase 2 trial, we consecutively enrolled patients at Northwestern Memorial Hospital (Chicago, IL, USA) who were aged younger than 60 years with diffuse systemic sclerosis, modified Rodnan skin scores (mRSS) of more than 14, and internal organ involvement or restricted skin involvement (mRSS <14) but coexistent pulmonary involvement. We randomly allocated patients 1:1 by use of a computer-generated sequence with a mixed block design (blocks of ten and four) to receive HSCT, 200 mg/kg intravenous cyclophosphamide, and 6·5 mg/kg intravenous rabbit antithymocyte globulin or to receive 1·0 g/m(2) intravenous cyclophosphamide once per month for 6 months. The primary outcome for all enrolled patients was improvement at 12 months' follow-up, defined as a decrease in mRSS (>25% for those with initial mRSS >14) or an increase in forced vital capacity by more than 10%. Patients in the control group with disease progression (>25% increase in mRSS or decrease of >10% in forced vital capacity) despite treatment with cyclophosphamide could switch to HSCT 12 months after enrolment. This study is registered with ClinicalTrials.gov, number NCT00278525.

FINDINGS

Between Jan 18, 2006, and Nov 10, 2009 we enrolled 19 patients. All ten patients randomly allocated to receive HSCT improved at or before 12 months' follow-up, compared with none of nine allocated to cyclophosphamide (odds ratio 110, 95% CI 14·04-∞; p=0·00001). Eight of nine controls had disease progression (without interval improvement) compared with no patients treated by HSCT (p=0·0001), and seven patients switched to HSCT. Compared with baseline, data for 11 patients with follow-up to 2 years after HSCT suggested that improvements in mRSS (p<0·0001) and forced vital capacity (p<0·03) persisted.

INTERPRETATION

Non-myeloablative autologous HSCT improves skin and pulmonary function in patients with systemic sclerosis for up to 2 years and is preferable to the current standard of care, but longer follow-up is needed.

FUNDING

None.

Computer-assisted detection of infectious lung diseases: a review

Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

PURPOSE

Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed.

METHODS

Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases.

RESULTS

In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side.

CONCLUSIONS

The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.

Multiple kernel learning for classification of diffuse lung disease using HRCT lung images

A novel algorithm is presented for classification of four patterns of diffuse lung disease: normal, emphysema, honeycombing and ground glass opacity, on the basis of textural analysis of high resolution computed tomography (HRCT) lung images. The algorithm incorporates scale-space features based on Gaussian derivative filters and multi-dimensional multi-scale features based on wavelet and contourlet transforms of the original images. The mean, standard deviation, skewness and kurtosis along with generalized Gaussian density are used to model the output of filters and transforms, and construct feature vectors. Multi-class multiple kernel learning (m-MKL) classifier is used to evaluate the performance of the feature extraction scheme. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512×512, 16 bits/pixel in DICOM format. The dataset contains 70,000 ROIs from slices already marked by experienced radiologists. The average sensitivity and specificity achieved is 94.16% and 98.68%, respectively.

Computer-aided detection of early interstitial lung diseases using low-dose CT images

This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.

Classification of diffuse lung disease patterns on high-resolution computed tomography by a bag of words approach

Visual inspection of diffuse lung disease (DLD) patterns on high-resolution computed tomography (HRCT) is difficult because of their high complexity. We proposed a bag of words based method on the classification of these textural patters in order to improve the detection and diagnosis of DLD for radiologists. Six kinds of typical pulmonary patterns were considered in this work. They were consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal tissue. Because they were characterized by both CT values and shapes, we proposed a set of statistical measure based local features calculated from both CT values and the eigen-values of Hessian matrices. The proposed method could achieve the recognition rate of 95.85%, which was higher comparing with one global feature based method and two other CT values based bag of words methods.

Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT

Identification and characterization of diffuse parenchyma lung disease (DPLD) patterns challenges computer-aided schemes in computed tomography (CT) lung analysis. In this study, an automated scheme for volumetric quantification of interstitial pneumonia (IP) patterns, a subset of DPLD, is presented, utilizing a multidetector CT (MDCT) dataset. Initially, lung-field segmentation is achieved by 3-D automated gray-level thresholding combined with an edge-highlighting wavelet preprocessing step, followed by a texture-based border refinement step. The vessel tree volume is identified and removed from lung field, resulting in lung parenchyma (LP) volume. Following, identification and characterization of IP patterns is formulated as a three-class pattern classification of LP into normal, ground glass, and reticular patterns, by means of k-nearest neighbor voxel classification, exploiting 3-D cooccurrence features. Performance of the proposed scheme in indentifying and characterizing ground glass and reticular patterns was evaluated by means of volume overlap (ground glass: 0.734 +/- 0.057, reticular: 0.815 +/- 0.037), true-positive fraction (ground glass: 0.638 +/- 0.055, reticular: 0.942 +/- 0.023) and false-positive fraction (ground glass: 0.361 +/- 0.027, reticular: 0.147 +/- 0.032) on five MDCT scans.

Computerized detection of diffuse lung disease in MDCT: the usefulness of statistical texture features

Accurate detection of diffuse lung disease is an important step for computerized diagnosis and quantification of this disease. It is also a difficult clinical task for radiologists. We developed a computerized scheme to assist radiologists in the detection of diffuse lung disease in multi-detector computed tomography (CT). Two radiologists selected 31 normal and 37 abnormal CT scans with ground glass opacity, reticular, honeycombing and nodular disease patterns based on clinical reports. The abnormal cases in our database must contain at least an abnormal area with a severity of moderate or severe level that was subjectively rated by the radiologists. Because statistical texture features may lack the power to distinguish a nodular pattern from a normal pattern, the abnormal cases that contain only a nodular pattern were excluded. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. The lungs were first segmented in each slice by use of a thresholding technique, and then divided into contiguous volumes of interest (VOIs) with a 64 x 64 x 64 matrix size. For each VOI, we determined and employed statistical texture features, such as run-length and co-occurrence matrix features, to distinguish abnormal from normal lung parenchyma. In particular, we developed new run-length texture features with clear physical meanings to considerably improve the accuracy of our detection scheme. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by the use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. We investigated the impact of new and conventional texture features, VOI size and the dimensionality for regions of interest on detecting diffuse lung disease. When we employed new texture features for 3D VOIs of 64 x 64 x 64 voxels, our system achieved the highest performance level: a sensitivity of 86% and a specificity of 90% for the detection of abnormal VOIs, and a sensitivity of 89% and a specificity of 90% for the detection of abnormal cases. Our computerized scheme would be useful for assisting radiologists in the diagnosis of diffuse lung disease.