Vessel tree reconstruction in thoracic CT scans with application to nodule detection

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.

Directionality quantification of in vitro grown dorsal root ganglion neurites using Fast Fourier Transform

BACKGROUND

The directionality analysis of the neurite outgrowths is an important methodology in neuroscience, especially in determining the behavior of neurons grown on silicon substrates.

NEW METHOD

Here we aimed to describe the methodology for quantification of the directionality of neurites based on the Fast Fourier Transform (FFT). We performed an image analysis case study that incorporates several software solutions and provides a rapid and precise technique to determine the directionality of neurites. In order to elicit aligned or unaligned neurite growth patterns, we used adult and newborn dorsal root ganglion (DRG) neurons grown on silicon micro-pillar substrates (MPS) with different pillar widths and spacing.

RESULTS

Compared to the control glass surfaces the neonatal and adult N52 and IB4 DRG neurites exhibited regular growth patterns more pronounced in the MPS regions with s narrow pillar spacing range. The neurites were preferentially oriented along three directional axes at 30°, 90°, and 150°.

CONCLUSION

The proposed methodology showed that FFT analysis is a reliable and easily reproducible method that can be successfully used to test growth patterns of DRG neurites grown on different substrates by considering the direction and angle of the neurites as well as the size of the soma.

CT-derived vessel segmentation for analysis of post-radiation therapy changes in vasculature and perfusion

Vessel segmentation in the lung is an ongoing challenge. While many methods have been able to successfully identify vessels in normal, healthy, lungs, these methods struggle in the presence of abnormalities. Following radiotherapy, these methods tend to identify regions of radiographic change due to post-radiation therapytoxicities as vasculature falsely. By combining texture analysis and existing vasculature and masking techniques, we have developed a novel vasculature segmentation workflow that improves specificity in irradiated lung while preserving the sensitivity of detection in the rest of the lung. Furthermore, radiation dose has been shown to cause vascular injury as well as reduce pulmonary function post-RT. This work shows the improvements our novel vascular segmentation method provides relative to existing methods. Additionally, we use this workflow to show a dose dependent radiation-induced change in vasculature which is correlated with previously measured perfusion changes (R² = 0.72) in both directly irradiated and indirectly damaged regions of perfusion. These results present an opportunity to extend non-contrast CT-derived models of functional change following radiation therapy.

Martins CM, Ferreira MM, Rego VG, Boaro JMC, Cipriano CLS, Silva AC, de Paiva AC, Junior GB, de Almeida JDS, Nunes RA, Mogami R, Gattass M. Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly advanced in research to try to understand this COVID-19 and sough solutions for the front-line professionals fighting this fatal disease. One of the tools to aid in the detection, diagnosis, treatment, and prevention of this disease is computed tomography (CT). CT images provide valuable information on how this new disease affects the lungs of patients. However, the analysis of these images is not trivial, especially when researchers are searching for quick solutions. Detecting and evaluating this disease can be tiring, time-consuming, and susceptible to errors. Thus, in this study, we aim to automatically segment infections caused by COVID19 and provide quantitative measures of these infections to specialists, thus serving as a support tool. We use a database of real clinical cases from Pedro Ernesto University Hospital of the State of Rio de Janeiro, Brazil. The method involves five steps: lung segmentation, segmentation and extraction of pulmonary vessels, infection segmentation, infection classification, and infection quantification. For the lung segmentation and infection segmentation tasks, we propose modifications to the traditional U-Net, including batch normalization, leaky ReLU, dropout, and residual block techniques, and name it as Residual U-Net. The proposed method yields an average Dice value of 77.1% and an average specificity of 99.76%. For quantification of infectious findings, the proposed method achieves results like that of specialists, and no measure presented a value of ρ < 0.05 in the paired t-test. The results demonstrate the potential of the proposed method as a tool to help medical professionals combat COVID-19. fight the COVID-19.

Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets

Each level of the coronary artery has different sizes and properties. The primary coronary arteries usually have high contrast to the background, while the secondary coronary arteries have low contrast to the background and thin structures. Furthermore, several small vessels are disconnected or broken up vascular segments. It is a challenging task to use a single model to segment all coronary artery sizes. To overcome this problem, we propose a novel segmenting method for coronary artery extraction from angiograms based on the primary and secondary coronary artery. Our method is a coarse-to-fine strategic approach for extracting coronary arteries in many different sizes. We construct the first U-net model to segment the main coronary artery extraction and build a new algorithm to determine the junctions of the main coronary artery with the secondary coronary artery. Using these junctions, we determine regions of the secondary coronary arteries (rectangular regions) for a secondary coronary artery-extracted segment with the second U-net model. The experiment result is 76.40% in terms of Dice coefficient on coronary X-ray datasets. The proposed approach presents its potential in coronary vessel segmentation.

On the performance of lung nodule detection, segmentation and classification

Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.

Learning-based algorithms for vessel tracking: A review

Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.

An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation

The main purpose of image enhancement technology is to improve the quality of the image to better assist those activities of daily life that are widely dependent on it like healthcare, industries, education, and surveillance. Due to the influence of complex environments, there are risks of insufficient detail and low contrast in some images. Existing enhancement algorithms are prone to overexposure and improper detail processing. This paper attempts to improve the treatment effect of Phase Stretch Transform (PST) on the information of low and medium frequencies. For this purpose, an image enhancement algorithm on the basis of fractional-order PST and relative total variation (FOPSTRTV) is developed to address the task. In this algorithm, the noise in the original image is removed by low-pass filtering, the edges of images are extracted by fractional-order PST, and then the images are fused with extracted edges through RTV. Finally, extensive experiments were used to verify the effect of the proposed algorithm with different datasets.

A robust and efficient framework for tubular structure segmentation in chest CT images

BACKGROUND

Tubular structure segmentation in chest CT images can reduce false positives (FPs) dramatically and improve the performance of nodules malignancy levels classification.

OBJECTIVE

In this study, we present a framework that can segment the pulmonary tubular structure regions robustly and efficiently.

METHODS

Firstly, we formulate a global tubular structure identification model based on Frangi filter. The model can recognize irregular vascular structures including bifurcation, small vessel, and junction, robustly and sensitively in 2D images. In addition, to segment the vessels from JVN, we design a local tubular structure identification model with a sliding window. Finally, we propose a multi-view voxel discriminating scheme on the basis of the previous two models. This scheme reduces the computational complexity of obtaining high entropy spatial tubular structure information.

RESULTS

Experimental results have shown that the proposed framework achieves TPR of 85.79%, FPR of 24.83%, and ACC of 84.47% with the average elapsed time of 162.9 seconds.

CONCLUSIONS

The framework provides an automated approach for effectively segmenting tubular structure from the chest CT images.

Computer aided detection of deep inferior epigastric perforators in computed tomography angiography scans

The deep inferior epigastric artery perforator (DIEAP) flap is the most common free flap used for breast reconstruction after a mastectomy. It makes use of the skin and fat of the lower abdomen to build a new breast mound either at the same time of the mastectomy or in a second surgery. This operation requires preoperative imaging studies to evaluate the branches - the perforators - that irrigate the tissue that will be used to reconstruct the breast mound. These branches will support tissue viability after the microsurgical ligation of the inferior epigastric vessels to the receptor vessels in the thorax. Usually through a computed tomography angiography (CTA), each perforator is manually identified and characterized by the imaging team, who will subsequently draw a map for the identification of the best vascular support for the reconstruction. In the current work we propose a semi-automatic methodology that aims at reducing the time and subjectivity inherent to the manual annotation. In 21 CTAs from patients proposed for breast reconstruction with DIEAP flaps, the subcutaneous region of each perforator was extracted, by means of a tracking procedure, whereas the intramuscular portion was detected through a minimum cost approach. Both were subsequently compared with the radiologist manual annotation. Results showed that the semi-automatic procedure was able to correctly detect the course of the DIEAPs with a minimum error (average error of 0.64 and 0.50 mm regarding the extraction of subcutaneous and intramuscular paths, respectively), taking little time to do so. The objective methodology is a promising tool in the automatic detection of perforators in CTA and can contribute to spare human resources and reduce subjectivity in the aforementioned task.

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.

Multiple Hidden Markov Model for Pathological Vessel Segmentation

One of the obstacles that prevent the accurate delineation of vessel boundaries is the presence of pathologies, which results in obscure boundaries and vessel-like structures. Targeting this limitation, we present a novel segmentation method based on multiple Hidden Markov Models. This method works with a vessel axis + cross-section model, which constrains the classifier around the vessel. The vessel axis constraint gives our method the potential to be both physiologically accurate and computationally effective. Focusing on pathological vessels, we reap the benefits of the redundant information embedded in multiple vessel-specific features and the good statistical properties coming with Hidden Markov Model, to cover the widest possible spectrum of complex situations. The performance of our method is evaluated on synthetic complex-structured datasets, where we achieve a 91% high overlap ratio. We also validate the proposed method on a real challenging case, segmentation of pathological abdominal arteries. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

Axis-Guided Vessel Segmentation Using a Self-Constructing Cascade-AdaBoost-SVM Classifier

One major limiting factor that prevents the accurate delineation of vessel boundaries has been the presence of blurred boundaries and vessel-like structures. Overcoming this limitation is exactly what we are concerned about in this paper. We describe a very different segmentation method based on a cascade-AdaBoost-SVM classifier. This classifier works with a vessel axis + cross-section model, which constrains the classifier around the vessel. This has the potential to be both physiologically accurate and computationally effective. To further increase the segmentation accuracy, we organize the AdaBoost classifiers and the Support Vector Machine (SVM) classifiers in a cascade way. And we substitute the AdaBoost classifier with the SVM classifier under special circumstances to overcome the overfitting issue of the AdaBoost classifier. The performance of our method is evaluated on synthetic complex-structured datasets, where we obtain high overlap ratios, around 91%. We also validate the proposed method on one challenging case, segmentation of carotid arteries over real clinical datasets. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

Vessel tree tracking in angiographic sequences

We present a method to track vessels in angiography [contrast filled vessels in two-dimensional (2-D) x-ray fluoroscopy]. Finding correspondence of a vessel tree from consecutive angiogram frames provides significant value in computer-aided clinical applications such as fast vessel tree segmentation, three-dimensional (3-D) vessel topology reconstruction from corresponding centerlines, cardiac motion understanding, etc. However, establishing an accurate vessel tree correspondence (vessel tree tracking) is a nontrivial problem due to nonlinear periodic cardiac and breathing motion in 2-D views, foreshortening, false bifurcations due to 3-D to 2-D projection, occlusion from other anatomies, etc. The vessel tree is represented by BSpline curves. The control points of the BSpline curves are landmarks that are the tracking targets. Our method maximizes the appearance similarity while preserving the vessel structure. A directed acyclic graph (DAG) is employed to represent the appearance and shape structure of the vessel tree: nodes from the DAG encode the appearance of the vessel tree landmarks, and the edges encode the relative locations between landmarks. The vessel tree tracking problem turns into finding the most similar tree from the DAG in the next frame, and it is solved using an efficient dynamic programming algorithm. We performed evaluations on 62 x-ray angiography sequences (above 1000 frames). Experiment results show our algorithm is robust to these challenges and delivers better performance, compared to four existing methods.

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning

Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.

Enhancement of Vascular Structures in 3D and 2D Angiographic Images

A number of imaging techniques are being used for diagnosis and treatment of vascular pathologies like stenoses, aneurysms, embolisms, malformations and remodelings, which may affect a wide range of anatomical sites. For computer-aided detection and highlighting of potential sites of pathology or to improve visualization and segmentation, angiographic images are often enhanced by Hessian based filters. These filters aim to indicate elongated and/or rounded structures by an enhancement function based on Hessian eigenvalues. However, established enhancement functions generally produce a response, which exhibits deficiencies such as poor and non-uniform response for vessels of different sizes and varying contrast, at bifurcations and aneurysms. This may compromise subsequent analysis of the enhanced images. This paper has three important contributions: i) reviews several established enhancement functions and elaborates their deficiencies, ii) proposes a novel enhancement function, which overcomes the deficiencies of the established functions, and iii) quantitatively evaluates and compares the novel and the established enhancement functions on clinical image datasets of the lung, cerebral and fundus vasculatures.

Automated integer programming based separation of arteries and veins from thoracic CT images

Automated computer-aided analysis of lung vessels has shown to yield promising results for non-invasive diagnosis of lung diseases. To detect vascular changes which affect pulmonary arteries and veins differently, both compartments need to be identified. We present a novel, fully automatic method that separates arteries and veins in thoracic computed tomography images, by combining local as well as global properties of pulmonary vessels. We split the problem into two parts: the extraction of multiple distinct vessel subtrees, and their subsequent labeling into arteries and veins. Subtree extraction is performed with an integer program (IP), based on local vessel geometry. As naively solving this IP is time-consuming, we show how to drastically reduce computational effort by reformulating it as a Markov Random Field. Afterwards, each subtree is labeled as either arterial or venous by a second IP, using two anatomical properties of pulmonary vessels: the uniform distribution of arteries and veins, and the parallel configuration and close proximity of arteries and bronchi. We evaluate algorithm performance by comparing the results with 25 voxel-based manual reference segmentations. On this dataset, we show good performance of the subtree extraction, consisting of very few non-vascular structures (median value: 0.9%) and merged subtrees (median value: 0.6%). The resulting separation of arteries and veins achieves a median voxel-based overlap of 96.3% with the manual reference segmentations, outperforming a state-of-the-art interactive method. In conclusion, our novel approach provides an opportunity to become an integral part of computer aided pulmonary diagnosis, where artery/vein separation is important.

Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering

BACKGROUND

Pulmonary nodules in computerized tomography (CT) images are potential manifestations of lung cancer. Segmentation of potential nodule objects is the first necessary and crucial step in computer-aided detection system of pulmonary nodules. The segmentation of various types of nodules, especially for ground-glass opacity (GGO) nodules and juxta-vascular nodules, present various challenges. The nodule with GGO characteristic possesses typical intensity inhomogeneity and weak edges, which is difficult to define the boundary; the juxta-vascular nodule is connected to a vessel, and they have very similar intensities. Traditional segmentation methods may result in the problems of boundary leakage and a small volume over-segmentation. This paper deals with the above mentioned problems.

METHODS

A novel segmentation method for pulmonary nodules is proposed, which uses an adaptive local region energy model with probability density function (PDF)-based similarity distance and multi-features dynamic clustering refinement method. Our approach has several novel aspects: (1) in the proposed adaptive local region energy model, the local domain for local energy model is selected adaptively based on k-nearest-neighbour (KNN) estimate method, and measurable distances between probability density functions of multi-dimension features with high class separability are used to build the cost function. (2) A multi-features dynamic clustering method is used for the segmentation refinement of juxta-vascular nodules, which is based on the nodule segmentation using active contour model (ACM) with adaptive local region energy and vessel segmentation using flow direction feature (FDF)-based region growing method. (3) it handles various types of nodules under a united framework.

RESULTS

The proposed method has been validated on a clinical dataset of 113 chest CT scans that contain 157 nodules determined by a ground truth reading process, and evaluating the algorithm on the provided data leads to an average Tanimoto/Jaccard error of 0.17, 0.20 and 0.24 for GGO, juxta-vascular and GGO juxta-vascular nodules, respectively.

CONCLUSIONS

Experimental results show desirable performances of the proposed method. The proposed segmentation method outperforms the traditional methods.

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.

Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels

This work is focused on application of a new technique in the first steps of computer-aided detection (CAD) of lung nodules. The scheme includes segmenting the lung volume and detecting most of the nodules with a low number of false positive (FP) objects. The juxtapleural nodules were properly included and the airways excluded in the lung segmentation. Among the suspicious regions obtained from the multiscale dot enhancement filter, those containing the center of nodule candidates, were determined. These center points were achieved from a 3D blob detector based on Laplacian of Gaussian kernels. Then the volumetric shape index (SI) that encodes the 3D local shape information was calculated for voxels in the determined regions. The performance of the scheme was evaluated by using 42 CT images from the Lung Image Database Consortium (LIDC). The results show that the average number of FPs reaches to 38.8 per scan with the sensitivity of 95.9% in the initial detections. The scheme is adaptable to detect nodules with wide variations in size, shape, intensity and location. Comparison of results with previously reported ones indicates that the proposed scheme can be satisfactory applied for initial detection of lung nodules in the chest CT images.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

A vessel active contour model for vascular segmentation

This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.

Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects

Introduction

The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance.

Methods

The relevant literature related to “CADe for lung cancer” was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized.

Discussion

Based on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research.

Conclusions

Further research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis.

Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated segmentation of pulmonary structures in thoracic CT has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes and the pulmonary segments. For each topic, the current state of the art is summarized, and topics for future research are identified.

CURVATURE-BASED CORRECTION ALGORITHM FOR AUTOMATIC LUNG SEGMENTATION ON CHEST CT IMAGES

In order to ameliorate the lung defects caused by missed juxtapleural nodules in lung segmentation on chest computed tomography (CT) images, we develop a Newton–Cotes-based smoothing algorithm (NCBS) which is used as a preliminary step to remove noises as many as possible. Next considering the crescent outline features of the lung, we propose a curvature-based correction algorithm (CBC) for the determination of the correction threshold. The application of the proposed algorithms is demonstrated in the process of lung segmentation and the experimental results on 25 real datasets are illustrated. Furthermore, some experiments are conducted to investigate the effects of the key parameters in CBC on the performances of lung segmentation so as to decide their optimal values. In addition, the CBC is compared with other methods analytically and experimentally. The overall results show that our proposed algorithm in lung segmentation excels the related methods on the capability of automatic selection of the correction threshold, as well as the performances of accuracy, efficiency and feasibility.

A novel approach for lung nodules segmentation in chest CT using level sets

A new variational level set approach is proposed for lung nodule segmentation in lung CT scans. A general lung nodule shape model is proposed using implicit spaces as a signed distance function. The shape model is fused with the image intensity statistical information in a variational segmentation framework. The nodule shape model is mapped to the image domain by a global transformation that includes inhomogeneous scales, rotation, and translation parameters. A matching criteria between the shape model and the image implicit representations is employed to handle the alignment process. Transformation parameters evolve through gradient descent optimization to handle the shape alignment process and hence mark the boundaries of the nodule “head.” The embedding process takes into consideration the image intensity as well as prior shape information. A nonparametric density estimation approach is employed to handle the statistical intensity representation of the nodule and background regions. The proposed technique does not depend on nodule type or location. Exhaustive experimental and validation results are demonstrated on 742 nodules obtained from four different CT lung databases, illustrating the robustness of the approach.

The self-overlap method for assessment of lung nodule morphology in chest CT

Surface morphology is an important indicator of malignant potential for solid-type lung nodules detected at CT, but is difficult to assess subjectively. Automated methods for morphology assessment have previously been described using a common measure of nodule shape, representative of the broad class of existing methods, termed area-to-perimeter-length ratio (APR). APR is static and thus highly susceptible to alterations by random noise and artifacts in image acquisition. We introduce and analyze the self-overlap (SO) method as a dynamic automated morphology detection scheme. SO measures the degree of change of nodule masks upon Gaussian blurring. We hypothesized that this new metric would afford equally high accuracy and superior precision than APR. Application of the two methods to a set of 119 patient lung nodules and a set of simulation nodules showed our approach to be slightly more accurate and on the order of ten times as precise, respectively. The dynamic quality of this new automated metric renders it less sensitive to image noise and artifacts than APR, and as such, SO is a potentially useful measure of cancer risk for solid-type lung nodules detected on CT.

Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification

The objective of this work is to develop a robust method for human abdominal aortic aneurysm (AAA) centerline detection that can contribute to the accurate computation of features for the prediction of AAA rupture risk. A semiautomatic algorithm is proposed for detecting the lumen centerline in contrast-enhanced abdominal computed tomography images based on online adaboost classifiers, which does not require prior image segmentation. The algorithm was developed and applied to thirty ruptured and thirty unruptured AAA image data sets and the tortuosities of the detected centerline were measured to assess the correlation between AAA tortuosity and the binary ruptured and unruptured labels. The lumen of each data set was segmented manually by a trained radiologist and the resulting centerlines of each data set were defined as the gold standard to evaluate the accuracy of the algorithm and to compare it against two widely used segmentation techniques. The average mean relative accuracy of the offline adaboost classifier is 91.9% with a standard deviation of 1.6%; for the online adaboost classifier it is 93.6% with a standard deviation of 1.9% (p<0.05). The online adaboost classifier outperforms the offline adaboost classifier while their computational costs are similar. Aneurysm tortuosity computed from an accurately derived lumen centerline using online adaboost is statistically higher for ruptured aneurysms compared to unruptured aneurysms, indicating that tortuosity can be used to assess rupture risk in the vascular clinic.

Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model

The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and “weak” local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.

A radial structure tensor and its use for shape-encoding medical visualization of tubular and nodular structures

The concept of curvature and shape-based rendering is beneficial for medical visualization of CT and MRI image volumes. Color-coding of local shape properties derived from the analysis of the local Hessian can implicitly highlight tubular structures such as vessels and airways, and guide the attention to potentially malignant nodular structures such as tumors, enlarged lymph nodes, or aneurysms. For some clinical applications, however, the evaluation of the Hessian matrix does not yield satisfactory renderings, in particular for hollow structures such as airways, and densely embedded low contrast structures such as lymph nodes. Therefore, as a complement to Hessian-based shape-encoding rendering, this paper introduces a combination of an efficient sparse radial gradient sampling scheme in conjunction with a novel representation, the radial structure tensor (RST). As an extension of the well-known general structure tensor, which has only positive definite eigenvalues, the radial structure tensor correlates position and direction of the gradient vectors in a local neighborhood, and thus yields positive and negative eigenvalues which can be used to discriminate between different shapes. As Hessian-based rendering, also RST-based rendering is ideally suited for GPU implementation. Feedback from clinicians indicates that shape-encoding rendering can be an effective image navigation tool to aid diagnostic workflow and quality assurance.

Solitary Pulmonary Nodule Detection on Thoracic CT Images through Object Continuity Analyses

Lung cancer has been a leading cause of death in the world, and it is known that prompt diagnosis and treatment may be the only chance for curing the cancer. Early lung cancer often presents as a solitary pulmonary nodule (SPN) and the timely detection of it is critical to save life from cancer death. In this paper, we present an effective method to detect SPNs on thoracic CT images through object continuity analyses. First, a lung region is segmented from other chest organs using morphological operations and thresholding techniques, and an initial set of candidate SPNs are identified. To represent the SPN, we define the rotation-invariant bounding rectangle (riBR) that tightly encloses an object. The subsequent processing is based on the riBR instead of an object itself to avoid the processing overhead. Next, non-nodule objects are pruned using geometric features and the object continuity analyses on a series of CT slice images. Through the analyses, cylinder-shaped non-nodule objects such as blood vessels and bronchia are eliminated and a final set of candidate SPNs is obtained. An experimental result shows that the proposed method works effectively in detecting SPNs. The application context addressed in this study is the pulmonary nodule detection but other application areas also can benefit.

Multiscale bi-Gaussian filter for adjacent curvilinear structures detection with application to vasculature images

The intensity or gray-level derivatives have been widely used in image segmentation and enhancement. Conventional derivative filters often suffer from an undesired merging of adjacent objects because of their intrinsic usage of an inappropriately broad Gaussian kernel; as a result, neighboring structures cannot be properly resolved. To avoid this problem, we propose to replace the low-level Gaussian kernel with a bi-Gaussian function, which allows independent selection of scales in the foreground and background. By selecting a narrow neighborhood for the background with regard to the foreground, the proposed method will reduce interference from adjacent objects simultaneously preserving the ability of intraregion smoothing. Our idea is inspired by a comparative analysis of existing line filters, in which several traditional methods, including the vesselness, gradient flux, and medialness models, are integrated into a uniform framework. The comparison subsequently aids in understanding the principles of different filtering kernels, which is also a contribution of this paper. Based on some axiomatic scale-space assumptions, the full representation of our bi-Gaussian kernel is deduced. The popular γ-normalization scheme for multiscale integration is extended to the bi-Gaussian operators. Finally, combined with a parameter-free shape estimation scheme, a derivative filter is developed for the typical applications of curvilinear structure detection and vasculature image enhancement. It is verified in experiments using synthetic and real data that the proposed method outperforms several conventional filters in separating closely located objects and being robust to noise.

Potential of computer-aided diagnosis to improve CT lung cancer screening

The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments.

Detection of microcalcification clusters using Hessian matrix and foveal segmentation method on multiscale analysis in digital mammograms

Mammography is the most efficient technique for detecting and diagnosing breast cancer. Clusters of microcalcifications have been mainly targeted as a reliable early sign of breast cancer and their earliest detection is essential to reduce the probability of mortality rate. Since the size of microcalcifications is very tiny and may be overlooked by the observing radiologist, we have developed a Computer Aided Diagnosis system for automatic and accurate cluster detection. A three-phased novel approach is presented in this paper. Firstly, regions of interest that corresponds to microcalcifications are identified. This can be achieved by analyzing the bandpass coefficients of the mammogram image. The suspicious regions are passed to the second phase, in which the nodular structured microcalcifications are detected based on eigenvalues of second order partial derivatives of the image and microcalcification pixels are segmented out by exploiting the foveal segmentation in multiscale analysis. Finally, by combining the responses coming out from the second order partial derivatives and the foveal method, potential microcalcifications are detected. The detection performance of the proposed method has been evaluated by using 370 mammograms. The detection method has a TP ratio of 97.76 % with 0.68 false positives per image. We have examined the performance of our computerized scheme using free-response operating characteristics curve.

COMPUTATIONAL VASCULAR MORPHOMETRY FOR THE ASSESSMENT OF PULMONARY VASCULAR DISEASE BASED ON SCALE-SPACE PARTICLES

We present a fully automatic computational vascular morphometry (CVM) approach for the clinical assessment of pulmonary vascular disease (PVD). The approach is based on the automatic extraction of the lung intraparenchymal vasculature using scale-space particles. Based on the detected features, we developed a set of image-based biomarkers for the assessment of the disease using the vessel radii estimation provided by the particle's scale. The biomarkers are based on the interrelation between vessel cross-section area and blood volume. We validate our vascular extraction method using simulated data with different complexity and we present results in 2,500 CT scans with different degrees of chronic obstructive pulmonary disease (COPD) severity. Results indicate that our CVM pipeline may track vascular remodeling present in COPD and it can be used in further clinical studies to assess the involvement of PVD in patient populations.

A novel computer-aided lung nodule detection system for CT images

PURPOSE

The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem.

METHODS

The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database.

RESULTS

The overall performance of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter.

CONCLUSIONS

A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.

A Hessian-based filter for vascular segmentation of noisy hepatic CT scans

PURPOSE

Extraction and enhancement of tubular structures are important in image processing applications, especially in the analysis of liver CT scans where delineation of vascular structures is needed for surgical planning. Portal vein cross-sections have circular or elliptical shapes, so an algorithm must accommodate both. A vessel segmentation method based on medial-axis points was developed and tested on portal veins in CT images.

METHODS

A medial-axis enhancement filter was developed. Consider a line passing through a point inside a tube and intersecting the edges of the tube. If the point is located on the medial axis, the distance of the point in the direction of the line to the edges of the tube will be equal. This feature was employed in a multi-scale framework to identify liver vessels. Dynamic thresholding was used to reduce noise sensitivity. The isotropic coefficient introduced by Pock et al. was used to reduce the response of the filter for asymmetric cross-sections.

RESULTS

Quantitative and qualitative evaluation of the proposed method were performed using both 2D/3D and synthetic/clinical datasets. Compared to other methods for medial-axis enhancement, our method produces better results in low-resolution CT images. Detection rate of the medial axis by the proposed method in a noisy image of standard deviation equal to 0.3 is 68% higher than prior methods.

CONCLUSION

A new Hessian-based method for medial axis vessel segmentation was developed and tested. This method produced superior results compared to prior methods. This new method has the potential for many applications of medial-axis enhancement.