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

Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography

Without performing biopsy that could lead physical damages to nerves and vessels, Computerized Tomography (CT) is widely used to diagnose the lung cancer due to the high sensitivity of pulmonary nodule detection. However, distinguishing pulmonary nodule in-between malignant and benign is still not an easy task. As the CT scans are mostly in relatively low resolution, it is not easy for radiologists to read the details of the scan image. In the past few years, the continuing rapid growth of CT scan analysis system has generated a pressing need for advanced computational tools to extract useful features to assist the radiologist in reading progress. Computer-aided detection (CAD) systems have been developed to reduce observational oversights by identifying the suspicious features that a radiologist looks for during case review. Most previous CAD systems rely on low-level non-texture imaging features such as intensity, shape, size or volume of the pulmonary nodules. However, the pulmonary nodules have a wide variety in shapes and sizes, and also the high visual similarities between benign and malignant patterns, so relying on non-texture imaging features is difficult for diagnosis of the nodule types. To overcome the problem of non-texture imaging features, more recent CAD systems adopted the supervised or unsupervised learning scheme to translate the content of the nodules into discriminative features. Such features enable high-level imaging features highly correlated with shape and texture. Convolutional neural networks (ConvNets), supervised methods related to deep learning, have been improved rapidly in recent years. Due to their great success in computer vision tasks, they are also expected to be helpful in medical imaging. In this thesis, a CAD based on a deep convolutional neural network (ConvNet) is designed and evaluated for malignant pulmonary nodules on computerized tomography. The proposed ConvNet, which is the core component of the proposed CAD system, is trained on the LUNGx challenge database to classify benign and malignant pulmonary nodules on CT. The architecture of the proposed ConvNet consists of 3 convolutional layers with maximum pooling operations and rectified linear units (ReLU) activations, followed by 2 denser layers with full-connectivities, and the architecture is carefully tailored for pulmonary nodule classification by considering the problems of over-fitting, receptive field, and imbalanced data. The proposed CAD system achieved the sensitivity of 0.896 and specificity of 8.78 at the optimal cut-off point of the receiver operating characteristic curve (ROC) with the area under the curve (AUC) of 0.920. The testing results showed that the proposed ConvNet achieves 10% higher AUC compared to the state-of-the-art work related to the unsupervised method. By integrating the proposed highly accurate ConvNet, the proposed CAD system also outperformed the other state-of-the-art ConvNets explicitly designed for diagnosis of pulmonary nodules detection or classification

Computerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135040/1/mp7129.pd

Computer-aided diagnosis tool for the detection of cancerous nodules in X-ray images

This thesis involves development of a computer-aided diagnosis (CAD) tool for the detection of cancerous nodules in X-ray images. Both cancerous and non-cancerous regions appear with little distinction on an X-ray image. For accurate detection of cancerous nodules, we need to differentiate the cancerous nodules from the non-cancerous. We developed an artificial neural network to differentiate them. Artificial neural networks (ANN) find a large application in the area of medical imaging. They work in a manner rather similar to the brain and have good decision making criteria when trained appropriately. We trained the neural network by the backpropagation algorithm and tested it with different images from a database of thoracic radiographs (chest X-rays) of dogs from the LSU Veterinary Medical Center. If we give X-ray images directly as input to the ANN, it incurs substantial complexity and training time for the network to process the images. A pre-processing stage involving some image enhancement techniques helps to solve the problem to a certain extent. The CAD tool developed in this thesis works in two stages. We pre-process the digitized images (by contrast enhancement, thresholding, filtering, and blob analysis) obtained after scanning the X-rays and then separate the suspected nodule areas (SNA) from the image by a segmentation process. We then input enhanced SNAs to the backpropagation-trained ANN. When given these enhanced SNAs, the neural network recognition accuracy, compared to unprocessed images as inputs, improved from 70% to 83.33%

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

Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images

Positron emission tomography (PET)-Computed tomography (CT) plays an important role in cancer management. As a multi-modal imaging technique it provides both functional and anatomical information of tumor spread. Such information improves cancer treatment in many ways. One important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information it provides helps radiation oncologists to better target the tumor region. However, currently most tumor delineations in radiotherapy planning are performed by manual segmentation, which consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In order to address this issue, a novel co-segmentation method is proposed in this work to enhance the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to differentiate and segment tumor regions from normal regions. On a combined dataset containing 29 patients with lung tumor, the combined method shows good segmentation results as well as good tumor recognition rate

Computer-aided Diagnosis of Pulmonary Nodules in Thoracic Computed Tomography.

Lung cancer is the leading cause of cancer death in the United States. The five-year survival rate is 15% because most patients present with advanced disease. If lung cancer is detected and treated at its earliest stage, the five-year survival rate has been reported as high as 92%. Computed tomography (CT) has been shown to be more sensitive than chest radiography in detecting abnormal lung lesions (nodules), especially when they are small. However, each thin-slice thoracic CT scan can contain hundreds of images. Large amounts of image data, radiologist fatigue, and diagnostic uncertainty may lead to missed cancers or unnecessary biopsies. We address these issues by developing a computer-aided diagnosis (CAD) system that would serve as a second reader for radiologists by analyzing nodules and providing a malignancy estimate using computer vision and machine learning techniques. To segment the nodules, we extended the active contour (AC) model to 3D by adding new energy terms. The classification accuracy, quantified by the area (Az) under the receiver operating characteristic curve, was used as the figure-of-merit to guide segmentation parameter optimization. The effect of CT acquisition parameters on 3DAC segmentation was systematically studied by imaging simulated nodules in chest phantoms. We conducted simulation studies to compare the relative performance of feature selection and classification methods and to examine the bias and variance introduced due to limited training sample sizes. We also designed new feature descriptors to describe the nodule surface, which were combined with texture and morphological features extracted from the nodule volume and the surrounding tissue to characterize the nodule. Stepwise feature selection was used to search for the subset of most effective features to be used in the linear discriminant analysis classifier. The CAD system achieved a test Az of 0.86±0.02 in a leave-one-case-out resampling scheme for 256 nodules from 152 patients. We conducted an observer study with six thoracic radiologists and found that their average Az in assessing nodule malignancy increased significantly (p<0.05) from 0.83±0.03 without CAD to 0.85±0.04 with CAD. These results indicate the potential usefulness of CAD as a second reader for radiologists in characterizing lung nodules.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60814/1/tway_1.pd

Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.

Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources

Machine learning approaches for lung cancer diagnosis.

The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide us with a huge amount of data, that could not be read easily by physicians and radiologists, with valuable information that could be used in smart ways to discover new knowledge from these vast quantities of data. Therefore, the design of computer-aided diagnostic (CAD) system, that can be approved for use in clinical practice that aid radiologists in diagnosis and detecting potential abnormalities, is of a great importance. This dissertation deals with the development of a CAD system for lung cancer diagnosis, which is the second most common cancer in men after prostate cancer and in women after breast cancer. Moreover, lung cancer is considered the leading cause of cancer death among both genders in USA. Recently, the number of lung cancer patients has increased dramatically worldwide and its early detection doubles a patient’s chance of survival. Histological examination through biopsies is considered the gold standard for final diagnosis of pulmonary nodules. Even though resection of pulmonary nodules is the ideal and most reliable way for diagnosis, there is still a lot of different methods often used just to eliminate the risks associated with the surgical procedure. Lung nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. A pulmonary nodule is the first indication to start diagnosing lung cancer. Lung nodules can be benign (normal subjects) or malignant (cancerous subjects). Large (generally defined as greater than 2 cm in diameter) malignant nodules can be easily detected with traditional CT scanning techniques. However, the diagnostic options for small indeterminate nodules are limited due to problems associated with accessing small tumors. Therefore, additional diagnostic and imaging techniques which depends on the nodules’ shape and appearance are needed. The ultimate goal of this dissertation is to develop a fast noninvasive diagnostic system that can enhance the accuracy measures of early lung cancer diagnosis based on the well-known hypotheses that malignant nodules have different shape and appearance than benign nodules, because of the high growth rate of the malignant nodules. The proposed methodologies introduces new shape and appearance features which can distinguish between benign and malignant nodules. To achieve this goal a CAD system is implemented and validated using different datasets. This CAD system uses two different types of features integrated together to be able to give a full description to the pulmonary nodule. These two types are appearance features and shape features. For the appearance features different texture appearance descriptors are developed, namely the 3D histogram of oriented gradient, 3D spherical sector isosurface histogram of oriented gradient, 3D adjusted local binary pattern, 3D resolved ambiguity local binary pattern, multi-view analytical local binary pattern, and Markov Gibbs random field. Each one of these descriptors gives a good description for the nodule texture and the level of its signal homogeneity which is a distinguishable feature between benign and malignant nodules. For the shape features multi-view peripheral sum curvature scale space, spherical harmonics expansions, and different group of fundamental geometric features are utilized to describe the nodule shape complexity. Finally, the fusion of different combinations of these features, which is based on two stages is introduced. The first stage generates a primary estimation for every descriptor. Followed by the second stage that consists of an autoencoder with a single layer augmented with a softmax classifier to provide us with the ultimate classification of the nodule. These different combinations of descriptors are combined into different frameworks that are evaluated using different datasets. The first dataset is the Lung Image Database Consortium which is a benchmark publicly available dataset for lung nodule detection and diagnosis. The second dataset is our local acquired computed tomography imaging data that has been collected from the University of Louisville hospital and the research protocol was approved by the Institutional Review Board at the University of Louisville (IRB number 10.0642). These frameworks accuracy was about 94%, which make the proposed frameworks demonstrate promise to be valuable tool for the detection of lung cancer