Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134780/1/mp0294.pd

Extracting Lungs from CT Images using Fully Convolutional Networks

Analysis of cancer and other pathological diseases, like the interstitial lung diseases (ILDs), is usually possible through Computed Tomography (CT) scans. To aid this, a preprocessing step of segmentation is performed to reduce the area to be analyzed, segmenting the lungs and removing unimportant regions. Generally, complex methods are developed to extract the lung region, also using hand-made feature extractors to enhance segmentation. With the popularity of deep learning techniques and its automated feature learning, we propose a lung segmentation approach using fully convolutional networks (FCNs) combined with fully connected conditional random fields (CRF), employed in many state-of-the-art segmentation works. Aiming to develop a generalized approach, the publicly available datasets from University Hospitals of Geneva (HUG) and VESSEL12 challenge were studied, including many healthy and pathological CT scans for evaluation. Experiments using the dataset individually, its trained model on the other dataset and a combination of both datasets were employed. Dice scores of $98.67\%\pm0.94\%$ for the HUG-ILD dataset and $99.19\%\pm0.37\%$ for the VESSEL12 dataset were achieved, outperforming works in the former and obtaining similar state-of-the-art results in the latter dataset, showing the capability in using deep learning approaches.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

Pulmonary nodule classification aided by clustering

Lung nodules can be detected through examining CT scans. An automated lung nodule classification system is presented in this paper. The system employs random forests as it base classifier. A unique architecture for classification-aided-by-clustering is presented. Four experiments are conducted to study the performance of the developed system. 5721 CT lung image slices from the LIDC database are employed in the experiments. According to the experimental results, the highest sensitivity of 97.92%, and specificty of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts

Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135074/1/mp4558.pd

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed

Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa

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

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

Coronary Artery Calcium Quantification in Contrast-enhanced Computed Tomography Angiography

Coronary arteries are the blood vessels supplying oxygen-rich blood to the heart muscles. Coronary artery calcium (CAC), which is the total amount of calcium deposited in these arteries, indicates the presence or the future risk of coronary artery diseases. Quantification of CAC is done by using computed tomography (CT) scan which uses attenuation of x-ray by different tissues in the body to generate three-dimensional images. Calcium can be easily spotted in the CT images because of its higher opacity to x-ray compared to that of the surrounding tissue. However, the arteries cannot be identified easily in the CT images. Therefore, a second scan is done after injecting a patient with an x-ray opaque dye known as contrast material which makes different chambers of the heart and the coronary arteries visible in the CT scan. This procedure is known as computed tomography angiography (CTA) and is performed to assess the morphology of the arteries in order to rule out any blockage in the arteries. The CT scan done without the use of contrast material (non-contrast-enhanced CT) can be eliminated if the calcium can be quantified accurately from the CTA images. However, identification of calcium in CTA images is difficult because of the proximity of the calcium and the contrast material and their overlapping intensity range. In this dissertation first we compare the calcium quantification by using a state-of-the-art non-contrast-enhanced CT scan method to conventional methods suggesting optimal quantification parameters. Then we develop methods to accurately quantify calcium from the CTA images. The methods include novel algorithms for extracting centerline of an artery, calculating the threshold of calcium adaptively based on the intensity of contrast along the artery, calculating the amount of calcium in mixed intensity range, and segmenting the artery and the outer wall. The accuracy of the calcium quantification from CTA by using our methods is higher than the non-contrast-enhanced CT thus potentially eliminating the need of the non-contrast-enhanced CT scan. The implications are that the total time required for the CT scan procedure, and the patient\u27s exposure to x-ray radiation are reduced

Normal imaging findings after aortic valve implantation on 18F-Fluorodeoxyglucose positron emission tomography with computed tomography

Background: To determine the normal perivalvular 18F-Fluorodeoxyglucose (18F-FDG) uptake on positron emission tomography (PET) with computed tomography (CT) within one year after aortic prosthetic heart valve (PHV) implantation. Methods: Patients with uncomplicated aortic PHV implantation were prospectively included and underwent 18F-FDG PET/CT at either 5 (± 1) weeks (group 1), 12 (± 2) weeks (group 2) or 52 (± 8) weeks (group 3) after implantation. 18F-FDG uptake around the PHV was scored qualitatively (none/low/intermediate/high) and quantitatively by measuring the maximum Standardized Uptake Value (SUVmax) and target to background ratio (SUVratio). Results: In total, 37 patients (group 1: n = 12, group 2: n = 12, group 3: n = 13) (mean age 66 ± 8 years) were prospectively included. Perivalvular 18F-FDG uptake was low (8/12 (67%)) and intermediate (4/12 (33%)) in group 1, low (7/12 (58%)) and intermediate (5/12 (42%)) in group 2, and low (8/13 (62%)) and intermediate (5/13 (38%)) in group 3 (P = 0.91). SUVmax was 4.1 ± 0.7, 4.6 ± 0.9 and 3.8 ± 0.7 (mean ± SD, P = 0.08), and SUVratio was 2.0 [1.9 to 2.2], 2.0 [1.8 to 2.6], and 1.9 [1.7 to 2.0] (median [IQR], P = 0.81) for groups 1, 2, and 3, respectively. Conclusion: Non-infected aortic PHV have similar low to intermediate perivalvular 18F-FDG uptake with similar SUVmax and SUVratio at 5, 12, and 52 weeks after implantation

Automated Pulmonary Artery Segmentation By Vessel Tracking In Low-Dose Computed Tomography Images

Low-dose computed tomography (CT) imaging provides a method for obtaining accurate anatomical information without the full radiation exposure inherent in standard CT protocols, and is primarily used in lung cancer screening. Segmentation of the pulmonary arteries from low-dose chest CT images is a vital first step in improving computer-aided detection of frequently missed pulmonary nodules near major arteries. This thesis presents the first fully automated method for segmenting the main pulmonary arterial trees in low-dose CT images. The correlation between the arterial and airway trees was used to develop an automated pulmonary artery seed point detector. The main basal pulmonary arteries are identified by searching for candidate vessels near known airways, using a progressive morphological opening method. The arteries are tracked into the lungs by means of a cylindrical vessel tracker that iteratively fits model cylinders to the CT image. Vessel bifurcations are detected by measuring the rate of change of vessel radii. Subsequent vessels are segmented by initiating new cylinder trackers at bifurcation points. Quantitative analysis of both the number of arteries and veins segmented, as well as the error per vessel, was accomplished with a novel evaluation metric called the Sparse Surface (SS) metric. The SS metric was developed to capture the details of the true vessel surface while reducing the ground-truth marking burden on the human user. This metric is a unique new tool for ground truth marking and segmentation validation, with particular importance in problems with complex geometries. The segmentation method and SS metric were applied to a dataset of seven CT images, and achieved an overall sensitivity of 0.62 and specificity of 0.90 of all manually identified vessels. The average root mean square error between the vessel surface and the segmentation surface was 0.63 mm, or less than 1 voxel. Additionally, seed points were detected automatically for a majority (80%) of cases with labeled airways. This method is an important first step towards robust pulmonary artery segmentation and artery/vein separation in low-dose chest CT, and is the first fully automated method designed for accomplishing this task