Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images

Study on high-precision three-dimensional reconstruction of pulmonary lesions and surrounding blood vessels based on CT images

The adoption of computerized tomography (CT) technology has significantly elevated the role of pulmonary CT imaging in diagnosing and treating pulmonary diseases. However, challenges persist due to the complex relationship between lesions within pulmonary tissue and the surrounding blood vessels. These challenges involve achieving precise three-dimensional reconstruction while maintaining accurate relative positioning of these elements. To effectively address this issue, this study employs a semi-automatic precise labeling process for the target region. This procedure ensures a high level of consistency in the relative positions of lesions and the surrounding blood vessels. Additionally, a morphological gradient interpolation algorithm, combined with Gaussian filtering, is applied to facilitate high-precision three-dimensional reconstruction of both lesions and blood vessels. Furthermore, this technique enables post-reconstruction slicing at any layer, facilitating intuitive exploration of the correlation between blood vessels and lesion layers. Moreover, the study utilizes physiological knowledge to simulate real-world blood vessel intersections, determining the range of blood vessel branch angles and achieving seamless continuity at internal blood vessel branch points. The experimental results achieved a satisfactory reconstruction with an average Hausdorff distance of 1.5 mm and an average Dice coefficient of 92%, obtained by comparing the reconstructed shape with the original shape,the approach also achieves a high level of accuracy in three-dimensional reconstruction and visualization. In conclusion, this study is a valuable source of technical support for the diagnosis and treatment of pulmonary diseases and holds promising potential for widespread adoption in clinical practice.

NCCT-CECT image synthesizers and their application to pulmonary vessel segmentation

BACKGROUND AND OBJECTIVES

Non-contrast CT (NCCT) and contrast-enhanced CT (CECT) are important diagnostic tools with distinct features and applications for chest diseases. We developed two synthesizers for the mutual synthesis of NCCT and CECT and evaluated their applications.

METHODS

Two synthesizers (S₁ and S₂) were proposed based on a generative adversarial network. S₁ generated synthetic CECT (SynCECT) from NCCT and S₂ generated synthetic NCCT (SynNCCT) from CECT. A new training procedure for synthesizers was proposed. Initially, the synthesizers were pretrained using self-supervised learning (SSL) and dual-energy CT (DECT) and then fine-tuned using the registered NCCT and CECT images. Pulmonary vessel segmentation from NCCT was used as an example to demonstrate the effectiveness of the synthesizers. Two strategies (ST₁ and ST₂) were proposed for pulmonary vessel segmentation. In ST₁, CECT images were used to train a segmentation model (Model-CECT), NCCT images were converted to SynCECT through S₁, and SynCECT was input to Model-CECT for testing. In ST₂, CECT data were converted to SynNCCT through S₂. SynNCCT and CECT-based annotations were used to train an additional model (Model-NCCT), and NCCT was input to Model-NCCT for testing. Three datasets, D1 (40 paired CTs), D2 (14 NCCTs and 14 CECTs), and D3 (49 paired DECTs), were used to evaluate the synthesizers and strategies.

RESULTS

For S₁, the mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were 14.60± 2.19, 1644± 890, 34.34± 1.91, and 0.94± 0.02, respectively. For S₂, they were 12.52± 2.59, 1460± 922, 35.08± 2.35, and 0.95± 0.02, respectively. Our synthesizers outperformed the counterparts of CycleGAN, Pix2Pix, and Pix2PixHD. The results of ablation studies on SSL pretraining, DECT pretraining, and fine-tuning showed that performance worsened (for example, for S₁, MAE increased to 16.53± 3.10, 17.98± 3.10, and 20.57± 3.75, respectively). Model-NCCT and Model-CECT achieved dice similarity coefficients (DSC) of 0.77 and 0.86 on D1 and 0.77 and 0.72 on D2, respectively.

CONCLUSIONS

The proposed synthesizers realized mutual and high-quality synthesis between NCCT and CECT images; the training procedures, including SSL pretraining, DECT pretraining, and fine-tuning, were critical to their effectiveness. The results demonstrated the usefulness of synthesizers for pulmonary vessel segmentation from NCCT images.

Facial expression recognition based on active region of interest using deep learning and parallelism

The automatic facial expression tracking method has become an emergent topic during the last few decades. It is a challenging problem that impacts many fields such as virtual reality, security surveillance, driver safety, homeland security, human-computer interaction, medical applications. A remarkable cost-efficiency can be achieved by considering some areas of a face. These areas are termed Active Regions of Interest (AROIs). This work proposes a facial expression recognition framework that investigates five types of facial expressions, namely neutral, happiness, fear, surprise, and disgust. Firstly, a pose estimation method is incorporated and to go along with an approach to rotate the face to achieve a normalized pose. Secondly, the whole face-image is segmented into four classes and eight regions. Thirdly, only four AROIs are identified from the segmented regions. The four AROIs are the nose-tip, right eye, left eye, and lips respectively. Fourthly, an info-image-data-mask database is maintained for classification and it is used to store records of images. This database is the mixture of all the images that are gained after introducing a ten-fold cross-validation technique using the Convolutional Neural Network. Correlations of variances and standard deviations are computed based on identified images. To minimize the required processing time in both training and testing the data set, a parallelism technique is introduced, in which each region of the AROIs is classified individually and all of them run in parallel. Fifthly, a decision-tree-level synthesis-based framework is proposed to coordinate the results of parallel classification, which helps to improve the recognition accuracy. Finally, experimentation on both independent and synthesis databases is voted for calculating the performance of the proposed technique. By incorporating the proposed synthesis method, we gain 94.499%, 95.439%, and 98.26% accuracy with the CK+ image sets and 92.463%, 93.318%, and 94.423% with the JAFFE image sets. The overall accuracy is 95.27% in recognition. We gain 2.8% higher accuracy by introducing a decision-level synthesis method. Moreover, with the incorporation of parallelism, processing time speeds up three times faster. This accuracy proves the robustness of the proposed scheme.

Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).

Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection

False positive reduction plays a key role in computer-aided detection systems for pulmonary nodule detection in computed tomography (CT) scans. However, this remains a challenge owing to the heterogeneity and similarity of anisotropic pulmonary nodules. In this study, a novel attention-embedded complementary-stream convolutional neural network (AECS-CNN) is proposed to obtain more representative features of nodules for false positive reduction. The proposed network comprises three function blocks: 1) attention-guided multi-scale feature extraction, 2) complementary-stream block with an attention module for feature integration, and 3) classification block. The inputs of the network are multi-scale 3D CT volumes due to variations in nodule sizes. Subsequently, a gradual multi-scale feature extraction block with an attention module was applied to acquire more contextual information regarding the nodules. A subsequent complementary-stream integration block with an attention module was utilized to learn the significantly complementary features. Finally, the candidates were classified using a fully connected layer block. An exhaustive experiment on the LUNA16 challenge dataset was conducted to verify the effectiveness and performance of the proposed network. The AECS-CNN achieved a sensitivity of 0.92 with 4 false positives per scan. The results indicate that the attention mechanism can improve the network performance in false positive reduction, the proposed AECS-CNN can learn more representative features, and the attention module can guide the network to learn the discriminated feature channels and the crucial information embedded in the data, thereby effectively enhancing the performance of the detection system.

An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans

Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.

False positive reduction in pulmonary nodule classification using 3D texture and edge feature in CT images

BACKGROUND

Pulmonary nodule detection can significantly influence the early diagnosis of lung cancer while is confused by false positives.

OBJECTIVE

In this study, we focus on the false positive reduction and present a method for accurate and rapid detection of pulmonary nodule from suspective regions with 3D texture and edge feature.

METHODS

This work mainly consists of four modules. Firstly, small pulmonary nodule candidates are preprocessed by a reconstruction approach for enhancing 3D image feature. Secondly, a texture feature descriptor is proposed, named cross-scale local binary patterns (CS-LBP), to extract spatial texture information. Thirdly, we design a 3D edge feature descriptor named orthogonal edge orientation histogram (ORT-EOH) to obtain spatial edge information. Finally, hierarchical support vector machines (H-SVMs) is used to classify suspective regions as either nodules or non-nodules with joint CS-LBP and ORT-EOH feature vector.

RESULTS

For the solitary solid nodule, ground-glass opacity, juxta-vascular nodule and juxta-pleural nodule, average sensitivity, average specificity and average accuracy of our method are 95.69%, 96.95% and 96.04%, respectively. The elapsed time in training and testing stage are 321.76 s and 5.69 s.

CONCLUSIONS

Our proposed method has the best performance compared with other state-of-the-art methods and is shown the improved precision of pulmonary nodule detection with computationaly low cost.

Solitary Pulmonary nodule segmentation based on pyramid and improved grab cut

BACKGROUND AND OBJECTIVE

Accurate segmentation of solitary pulmonary nodule of digital radiography image is essential for lesion appearance measurement and medical follow-up. However, the imaging characteristics of digital radiography, the inhomogeneity and fuzzy contours of nodules often lead to poor performances. This work aims to develop a segmentation framework that satisfies the requirements of accurate segmentation.

METHODS

In this work, an interactive segmentation method which combined the enhanced total-variance pyramid and improved Grab cut was proposed to improve the performance of nodule segmentation. The edge-preserving multi-resolution pyramid structure did the rough segmentation on low resolution images, which provided contour nearby curves to initial the following accuracy segmentation and shortened the time of energy decreasing. With the multiscale information being incorporated to optimize the edge term and improve the appearance model, a novel Gibbs energy functional was constructed to extract the nodule in a proper scale. By introducing the multiscale processing and optimizing the energy terms, the proposed method could overcome the inhomogeneity and fuzzy contours.

RESULTS

For evaluation of the nodule segmentation, quantitative metrics such as precision, intersection over union, and dice similarity coefficient were introduced and compared in the experimental part. The proposed solitary pulmonary nodule segmentation method produced the results with mean values of precision 0.957, dice similarity coefficient 0.933, and intersection over union 0.891, respectively. And the corresponding standard deviation values were 0.041, 0.047, and 0.045.

CONCLUSIONS

From the quantitative assessment and comparison in the experiments, the proposed method achieved a competitive performance in accuracy and stability, even in the cases with low contrast and fuzzy contours.

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

BACKGROUND

Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules.

OBJECTIVES

The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx.

METHODS

This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules.

RESULTS

A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper.

CONCLUSION

The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

Hybrid Airway Segmentation Using Multi-Scale Tubular Structure Filters and Texture Analysis on 3D Chest CT Scans

Airway diseases are frequently related to morphological changes that may influence lung physiology. Accurate airway region segmentation may be useful for quantitative evaluation of disease prognosis and therapy efficacy. The information can also be applied to understand the fundamental mechanisms of various lung diseases. We present a hybrid method to automatically segment the airway regions on 3D volume chest computed tomography (CT) scans. This method uses multi-scale filtering and support vector machine (SVM) classification. The proposed scheme is comprised of two hybrid steps. First, a tubular structure-based multi-scale filter is applied to find the initial candidate airway regions. Second, for identifying candidate airway regions using the fuzzy connectedness technique, the small and disconnected branches of airway regions are detected using SVM classification trained to differentiate between airway and non-airway regions through texture analysis of user-defined landmark points. For development and evaluation of the method, two datasets were incorporated: (1) 55 lung-CT volumes from the Korean Obstructive Lung Disease (KOLD) Cohort Study and (2) 20 cases from the publicly open database (EXACT'09). The average tree-length detection rates of EXACT'09 and KOLD were 56.9 ± 11.0 and 70.5 ± 8.98, respectively. Comparison of the results for the EXACT'09 data set between the presented method and other methods revealed that our approach was a high performer. The method limitations were higher false-positive rates than those of the other methods and risk of leakage. In future studies, application of a convolutional neural network will help overcome these shortcomings.

Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data

BACKGROUND

To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation.

METHODS

The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once (YOLO) v3, as a deep learning approach. This method is featured by two distinct properties: (I) an automatic multi-scale feature extractor for nodule feature screening, and (II) a feature-based bounding box generator for nodule localization and diameter estimation. Two independent studies were performed to train and evaluate this CAD method. One study comprised of a computer simulation that utilized computer-based ground truth. In this study, 300 CT scans were simulated by Cardiac-torso (XCAT) digital phantom. Spherical nodules of various sizes (i.e., 3-10 mm in diameter) were randomly implanted within the lung region of the simulated images-the second study utilized human-based ground truth in patients. The CAD method was developed by CT scans sourced from the LIDC-IDRI database. CT scans with slice thickness above 2.5 mm were excluded, leaving 888 CT images for analysis. A 10-fold cross-validation procedure was implemented in both studies to evaluate network hyper-parameterization and generalization. The overall accuracy of the CAD method was evaluated by the detection sensitivities, in response to average false positives (FPs) per image. In the patient study, the detection accuracy was further compared against 9 recently published CAD studies using free-receiver response operating characteristic (FROC) curve analysis. Localization and diameter estimation accuracies were quantified by the mean and standard error between the predicted value and ground truth.

RESULTS

The average results among the 10 cross-validation folds in both studies demonstrated the CAD method achieved high detection accuracy. The sensitivity was 99.3% (FPs =1), and improved to 100% (FPs =4) in the simulation study. The corresponding sensitivities were 90.0% and 95.4% in the patient study, displaying superiority over several conventional and CNN-based lung nodule CAD methods in the FROC curve analysis. Nodule localization and diameter estimation errors were less than 1 mm in both studies. The developed CAD method achieved high computational efficiency: it yields nodule-specific quantitative values (i.e., number, existence confidence, central coordinates, and diameter) within 0.1 s for 2D CT slice inputs.

CONCLUSIONS

The reported results suggest that the developed lung pulmonary nodule CAD method possesses high accuracies of nodule localization and diameter estimation. The high computational efficiency enables its potential clinical application in the future.

Spectral analysis for pulmonary nodule detection using the optimal fractional S-Transform

Different frequency components of the lung, which have not been fully considered in traditional computer-aided detection systems for pulmonary nodules, can cause heterogeneous energy distribution. Hence, spectral analysis, which is an important time-frequency representation tool, is utilized to characterize the frequency-dependent energy responses of nodules. In this study, a novel spectral-analysis-based method for nodule candidate detection is presented. The optimal fractional S-transform is applied to transform raw computed tomography images from the spatial to time-frequency domain. Next, a time-frequency cube is decomposed using spectral decomposition to a frequency-dependent energy slice. Subsequently, an energy distribution is obtained by the Teager-Kaiser energy (TKE) to characterize the nodules. Finally, nodule candidates are detected using rule-based and threshold algorithms in the TKE image. The proposed method is validated on a clinical CT data set from Sichuan Provincial People's Hospital. The signal-to-clutter ratio (SCR) increases by 35.5% with respect to raw CT slices. Furthermore, the proposed method exhibits a sensitivity of 97.87%, with only 6.8 false positives per slice. The total number of nodule candidates has an average reduction of 50%. The results indicate that the time-frequency features can effectively characterize solid nodules. Moreover, the proposed method demonstrates accurate detection and can reduce the number of false positive efficiently.

Segmentation and suppression of pulmonary vessels in low-dose chest CT scans

PURPOSE

The suppression of pulmonary vessels in chest computed tomography (CT) images can enhance the conspicuity of lung nodules, thereby improving the detection rate of early lung cancer. This study aimed to develop two key techniques in vessel suppression, that is, segmentation and removal of pulmonary vessels while preserving the nodules.

METHODS

Pulmonary vessel segmentation and removal methods in CT images were developed. The vessel segmentation method used a framework of two cascaded convolutional neural networks (CNNs). A bi-class segmentation network was utilized in the first step to extract high-intensity structures, including both vessels and nonvascular tissues such as nodules. A tri-class segmentation network was employed in the second step to distinguish the vessels from nonvascular tissues (mainly nodules) and the lung parenchyma. In the vessel removal method, the voxels in the segmented vessels were replaced with randomly selected voxels from the surrounding lung parenchyma. The dataset in this study comprised 50 three-dimensional (3D) low-dose chest CT images. The labels for vessel and nodule segmentation were annotated with a semi automatic approach. The two cascaded networks for pulmonary vessel segmentation were trained with CT images of 40 cases and tested with CT images of ten cases. Pulmonary vessels were removed from the ten testing scans based on the predicted segmentation results. In addition to qualitative evaluation to the effects of segmentation and removal, the segmentation results were quantitatively evaluated using Dice coefficient (DICE), Jaccard index (JAC), and volumetric similarity (VS) and the removal results were evaluated using contrast-to-noise ratio (CNR).

RESULTS

In the first step of vessel segmentation, the mean DICE, JAC, and VS for high-intensity tissues, including both vessels and nodules, were 0.943, 0.893, and 0.991, respectively. In the second step, all the nodules were separated from the vessels, and the mean DICE, JAC, and VS for the vessels were 0.941, 0.890, and 0.991, respectively. After vessel removal, the mean CNR for nodules was improved from 4.23 (6.26 dB) to 6.95 (8.42 dB).

CONCLUSIONS

Quantitative and qualitative evaluations demonstrated that the proposed method achieved a high accuracy for pulmonary vessel segmentation and a good effect on pulmonary vessel suppression.

An Approach for Pulmonary Vascular Extraction from Chest CT Images

Pulmonary vascular extraction from chest CT images plays an important role in the diagnosis of lung disease. To improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular extraction approach is proposed in this study. First, the lung tissue is extracted from chest CT images by region-growing and maximum between-class variance methods. Then the holes of the extracted region are filled by morphological operations to obtain complete lung region. Second, the points of the pulmonary vascular of the middle slice of the chest CT images are extracted as the original seed points. Finally, the seed points are spread throughout the lung region based on the fast marching method to extract the pulmonary vascular in the gradient image. Results of pulmonary vascular extraction from chest CT image datasets provided by the introduced approach are presented and discussed. Based on the ground truth pixels and the resulting quality measures, it can be concluded that the average accuracy of this approach is about 90%. Extensive experiments demonstrate that the proposed method has achieved the best performance in pulmonary vascular extraction compared with other two widely used methods.

Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extraction and grouping, (3) false positives reduction for the non-vessel tree group, and (4) classification for the vessel tree group. Lung segmentation is performed first. Then, 3D labeling technology is used to divide nodule candidates into two groups. For the non-vessel tree group, nodule candidates are classified as true nodules at the false positive reduction stage if the candidates survive the rule-based classifier and are not screened out by the dot filter. For the vessel tree group, nodule candidates are extracted using dot filter. Next, RSFS feature selection is used to select the most discriminating features for classification. Finally, WSVM with an undersampling approach is adopted to discriminate true nodules from vessel bifurcations in vessel tree group. The proposed method was evaluated on 154 thin-slice scans with 204 nodules in the LIDC database. The performance of the proposed CAD scheme yielded a high sensitivity (87.81%) while maintaining a low false rate (1.057 FPs/scan). The experimental results indicate the performance of our method may be better than the existing methods.

Automatic nodule detection for lung cancer in CT images: A review

Automatic lung nodule detection has great significance for treating lung cancer and increasing patient survival. This work summarizes a critical review of recent techniques for automatic lung nodule detection in computed tomography images. This review indicates the current tendency and obtained progress as well as future challenges in this field. This research covered the databases including Web of Science, PubMed, and the Press, including IEEE Xplore and Science Direct, up to May 2018. Each part of the paper is summarized carefully in terms of the method and validation results for better comparison. Based on the results, some techniques show better performance for lung nodule detection. However, researchers should pay attention to the existing challenges, such as high sensitivity with a low false positive rate, large and different patient databases, developing or optimizing the detection technique of various types of lung nodules with different sizes, shapes, textures and locations, combining electronic medical records and picture archiving and communication systems, building efficient feature sets for better classification and promoting the cooperation and communication between academic institutions and medical organizations. We believe that automatic computer-aided detection systems will be developed with strong robustness, high efficiency and security assurance. This review will be helpful for professional researchers and radiologists to further learn about the latest techniques in computer-aided detection systems.

Voxel-based comparative analysis of lung lesions in CT for therapeutic purposes

Lung cancer remains as one of the most incident types of cancer throughout the world. Temporal evaluation has become a very useful tool when one wishes to analyze some malignancy-indicating behavior. The objective of the present work is to detect changes in the local densities of lung lesions over time (follow-up analysis). From the detected changes, local information as well as extent region of changes can complement the studies regarding the malignant or benign nature of the lesion. Based on this idea, we attempt to use techniques that allow the observation of changes in the lesion over time, based on remote sensing techniques which highlight changes occurring in the environment. The techniques used were the image differencing, image rationing, median filtering, image regression and the fuzzy XOR operator. Based on the global measurement of change percentage in the density, we found density variations which were considered significant in a range from 2.22 to 36.57 % of the volume of the lesion. The results achieved are promising since, besides the visual aspects of the changes in density of the lung lesion over time, we managed to quantify these changes and compare them by volumetric analysis, a more commonly used technique for analysis of changes in lung lesions.

Automatic 3D pulmonary nodule detection in CT images: A survey

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.

Adhesion Pulmonary Nodules Detection Based on Dot-Filter and Extracting Centerline Algorithm

A suspected pulmonary nodule detection method was proposed based on dot-filter and extracting centerline algorithm. In this paper, we focus on the distinguishing adhesion pulmonary nodules attached to vessels in two-dimensional (2D) lung computed tomography (CT) images. Firstly, the dot-filter based on Hessian matrix was constructed to enhance the circular area of the pulmonary CT images, which enhanced the circular suspected pulmonary nodule and suppresses the line-like areas. Secondly, to detect the nondistinguishable attached pulmonary nodules by the dot-filter, an algorithm based on extracting centerline was developed to enhance the circle area formed by the end or head of the vessels including the intersection of the lines. 20 sets of CT images were used in the experiments. In addition, 20 true/false nodules extracted were used to test the function of classifier. The experimental results show that the method based on dot-filter and extracting centerline algorithm can detect the attached pulmonary nodules accurately, which is a basis for further studies on the pulmonary nodule detection and diagnose.

Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

Background

Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.

Methods

The novel method is based on oriented Gaussian derivatives filter with seven orientations, combined with Fuzzy C-Means (FCM) clustering and thresholding to refine the lung region. In addition, a new algorithm to automatically generate a threshold value for each Gaussian response is also proposed. The algorithms are applied to both PA and AP chest radiographs from both public JSRT dataset and our private datasets from collaborative hospital. Two pre-processing blocks are introduced to standardize the images from different machines. Comparisons with the previous works found in the literature on JSRT dataset shows that our method gives a reasonably good result. We also compare our algorithm with other unsupervised methods to provide fairly comparative measures on the performances for all datasets.

Results

Performance measures (accuracy, F-score, precision, sensitivity and specificity) for the segmentation of lung in public JSRT dataset are above 0.90 except for the overlap measure is 0.87. The standard deviations for all measures are very low, from 0.01 to 0.06. The overlap measure for the private image database is 0.81 (images from standard machine) and 0.69 (images from two mobile machines). The algorithm is fully automated and fast, with the average execution time of 12.5 s for 512 by 512 pixels resolution.

Conclusions

Our proposed method is fully automated, unsupervised, with no training or learning stage is necessary to segment the lungs taken using both a standard machine and two different mobile machines. The proposed pre-processing blocks are significantly useful to standardize the radiographs from mobile machines. The algorithm gives good performance measures, robust, and fast for the application of the CBMIRS.

Computerized segmentation of pulmonary nodules depicted in CT examinations using freehand sketches

PURPOSE

To aid a consistent segmentation of pulmonary nodules, the authors describe a novel computerized scheme that utilizes a freehand sketching technique and an improved break-and-repair strategy.

METHODS

This developed scheme consists of two primary parts. The first part is freehand sketch analysis, where the freehand sketching not only serves a natural way of specifying the location of a nodule, but also provides a mechanism for inferring adaptive information (e.g., the mass center, the density, and the size) in regard to the nodule. The second part is an improved break-and-repair strategy. The improvement avoids the time-consuming ray-triangle intersections using spherical bins and replaces the original global implicit surface reconstruction with a local implicit surface fitting and blending scheme. The performance of this scheme, including accuracy and consistence, was assessed using 50 CT examinations in the Lung Image Database Consortium (LIDC). For each of these examinations, a single nodule was selected under the aid of a publically available tool to assure these nodules were diverse in size, location, and density. Two radiologists were asked to use the developed tool to segment these nodules twice at different times (at least three months apart). A Hausdorff distance based method was used to assess the discrepancies (agreements) between the computerized results and the results by the four radiologists in the LIDC as well as the inter- and intrareader agreements in freehand sketching.

RESULTS

The maximum and mean discrepancies in boundary outlines between the computerized scheme and the radiologists were 2.73 ± 1.32 mm and 1.01 ± 0.47 mm, respectively. When the nodules were classified (binned) into different size ranges, the maximum errors ranged from 1.91 to 4.13 mm; but smaller nodules had larger percentage discrepancies in term of size. Under the aid of the developed scheme, the inter- and intrareader variability in averaged maximum discrepancy across all types of pulmonary nodules were consistently smaller than 0.15 ± 0.07 mm. The computational cost in time of segmenting a pulmonary nodule ranged from 0.4 to 2.3 s with an average of 1.1 s for a typical desktop computer.

CONCLUSIONS

The experiments showed that this scheme could achieve a reasonable performance in nodule segmentation and demonstrated the merits of incorporating freehand sketching into pulmonary nodule segmentation.

Quantification of tortuosity and fractal dimension of the lung vessels in pulmonary hypertension patients

Pulmonary hypertension (PH) can result in vascular pruning and increased tortuosity of the blood vessels. In this study we examined whether automatic extraction of lung vessels from contrast-enhanced thoracic computed tomography (CT) scans and calculation of tortuosity as well as 3D fractal dimension of the segmented lung vessels results in measures associated with PH.

In this pilot study, 24 patients (18 with and 6 without PH) were examined with thorax CT following their diagnostic or follow-up right-sided heart catheterisation (RHC). Images of the whole thorax were acquired with a 128-slice dual-energy CT scanner. After lung identification, a vessel enhancement filter was used to estimate the lung vessel centerlines. From these, the vascular trees were generated. For each vessel segment the tortuosity was calculated using distance metric. Fractal dimension was computed using 3D box counting. Hemodynamic data from RHC was used for correlation analysis.

Distance metric, the readout of vessel tortuosity, correlated with mean pulmonary arterial pressure (Spearman correlation coefficient: ρ = 0.60) and other relevant parameters, like pulmonary vascular resistance (ρ = 0.59), arterio-venous difference in oxygen (ρ = 0.54), arterial (ρ = −0.54) and venous oxygen saturation (ρ = −0.68). Moreover, distance metric increased with increase of WHO functional class. In contrast, 3D fractal dimension was only significantly correlated with arterial oxygen saturation (ρ = 0.47).

Automatic detection of the lung vascular tree can provide clinically relevant measures of blood vessel morphology. Non-invasive quantification of pulmonary vessel tortuosity may provide a tool to evaluate the severity of pulmonary hypertension.

Trial Registration

ClinicalTrials.gov NCT01607489

3-D vascular skeleton extraction and decomposition

We introduce a novel vascular skeleton extraction and decomposition technique for computer-assisted diagnosis and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and present a methodology for weighted convex approximations. Decomposed vessel structures are then grouped using the vessel skeleton, extracted using a Laplace-based operator. The method is validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for vessel skeleton extraction. The proposed method produced promising results with an estimated 80.5% of the vessel sections correctly decomposed and 92.9% of the vessel sections having the correct number of skeletal branches, identified by a clinical radiological expert. Next, the method was validated on longitudinal study data from n = 4 subjects, where vascular skeleton extraction and decomposition was performed. Volumetric and surface area comparisons were made between expert segmented sections and the proposed approach on sections containing aneurysms. Results suggest that the method is able to detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

AN IMPROVED RANDOM WALK SEGMENTATION ON THE LUNG NODULES

In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM (Fuzzy C Means) segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.

3D Vascular Decomposition and Classification for Computer-Aided Detection

In this study, we propose a weighted approximate convex decomposition (WACD) and classification methodology for computer-aided detection (CADe) and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and introduce a methodology for compact geometric decomposition. The classification of decomposed vessel sections is performed using the most relevant eigenvalues obtained through feature selection. The method was validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for classification. The proposed method produced promising results with an estimated 81.5% of the vessel sections correctly decomposed. Recursive feature elimination was performed to find the most compact and informative set of features. We showed that the selected subset of eigenvalues produces minimum error and improved classifier precision. The method was also validated on a longitudinal study of four cases having internal cerebral aneurysms. Volumetric and surface area comparisons were made between expert-segmented sections and WACD classified sections containing aneurysms. Results suggest that the approach is able to classify and detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

Vascular decomposition using weighted approximate convex decomposition

OBJECTIVE

Stroke treatment often requires analysis of vascular pathology evaluated using computed tomography (CT) angiography. Due to vascular variability and complexity, finding precise relationships between vessel geometries and arterial pathology is difficult. A new convex shape decomposition strategy was developed to understand complex vascular structures and synthesize a weighted approximate convex decomposition (WACD) method for vascular decomposition in computer-aided diagnosis.

MATERIALS AND METHODS

The vascular tree is decomposed into optimal number of components (determined by an expert). The decomposition is based on two primary features of vascular structures: (i) the branching factor that allows structural decomposition and (ii) the concavity over the vessel surface seen primarily at the site of an aneurysm. Such surfaces are decomposed into subcomponents. Vascular sections are reconstructed using CT angiograms. Next the dual graph is constructed, and edge weights for the graph are computed from shape indices. Graph vertices are iteratively clustered by a mesh decimation operator, while minimizing a cost function related to concavity.

RESULTS

The method was validated by first comparing results with an approximate convex decomposition (ACD) method and next on vessel sections (n = 177) whose number of clusters (ground truth) was predetermined by an expert. In both cases, WACD produced promising results with 84.7 % of the vessel sections correctly clustered and when compared with ACD produced a more effective decomposition. Next, the algorithm was validated in a longitudinal study data of 4 subjects where volumetric and surface area comparisons were made between expert segmented sections and WACD decomposed sections that contained aneurysms. The results showed a mean error rate of 7.8 % for volumetric comparisons and 10.4 % for surface area comparisons.

CONCLUSION

Decomposition of the cerebral vasculature from CT angiograms into a geometrically optimal set of convex regions may be useful for computer-assisted diagnosis. A new WACD method capable of decomposing complex vessel structures, including bifurcations and aneurysms, was developed and tested with promising results.