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

Computer aided diagnosis for severity assessment of pneumoconiosis using CT images

240,000 participants have a screening for diagnosis of pneumoconiosis every year in Japan. Radiograph is used for staging of severity in pneumoconiosis worldwide. This paper presents a method for quantitative assessment of severity in pneumoconiosis using both size and frequency of lung nodules that detected by thin-section CT images. This method consists of three steps. First, thoracic organs (body, ribs, spine, trachea, bronchi, lungs, heart, and pulmonary blood vessels) are segmented. Second, lung nodules that have radius over 1.5mm are detected. These steps used functions of our developed computer aided detection system of chest CT images. Third, severity in pneumoconiosis is quantified using size and frequency of lung nodules. This method was applied to nine pneumoconiosis patients. The initial results showed that proposed method can assess severity in pneumoconiosis quantitatively. This paper demonstrates effectiveness of our method in diagnosis and prognosis of pneumoconiosis in CT screening

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

Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate

An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods

Detection of pulmonary nodules by computer-aided diagnosis in multidetector computed tomography: preliminary study of 24 cases

OBJECTIVES: To evaluate the performance of a computer program designed to facilitate the detection of pulmonary nodules using multidetector computed tomography (MDCT) scans of the chest. METHODS: We evaluated 24 consecutive MDCT scans of the chest at the Fleury Diagnostic Imaging Center during the period from October 7 to October 19 of 2006, using a 64-channel CT scanner. The study comprised 12 females and 12 males, ranging from 35 to 77 years of age (mean, 57.9 years). Double reading and a computer-aided diagnosis (CAD) system were used in order to perform two independent analyses of the data. The nodules found using both methods were recorded, and the data were compared. RESULTS: The total sensitivity of CAD for the detection of nodules was 16.5%, increasing to 55% when nodules 1 cm. More than 99% of true nodules detected by CAD were registered in the image double reading process. CONCLUSIONS: In this preliminary 24-case study, the sensitivity of computer program tested was not significantly greater than that of the double-reading process that is routinely performed in this facility.OBJETIVOS: Avaliar o desempenho de um programa para auxílio na detecção de nódulos pulmonares em tomografia computadorizada com múltiplos detectores (TCMD). MÉTODOS: Foram avaliadas 24 tomografias computadorizadas de tórax consecutivas realizadas no Centro de Medicina Diagnóstica Fleury no período de 07/10/2006 a 19/10/2006 usando um tomógrafo helicoidal multidetectores de 64 canais. O estudo compreendeu 12 pacientes do sexo feminino e 12 do sexo masculino, com idades variando entre 35 e 77 anos, idade média de 57,9. As imagens foram analisadas independentemente pelo método da dupla leitura e pelo programa diagnóstico auxiliado por computador (DAC). Os nódulos encontrados nos diferentes processos foram registrados e os dados comparados. RESULTADOS: A sensibilidade total da detecção de nódulos pelo DAC nesse trabalho foi de 16,5%, 55% excluindo os nódulos medindo 1 cm. Menos de 1% dos nódulos verdadeiros destacados pelo DAC não haviam sido registrados no processo de dupla leitura. CONCLUSÕES: Neste trabalho preliminar de 24 casos, o programa testado não conseguiu superar de forma significativa a sensibilidade da dupla leitura realizada de rotina neste serviço.Universidade Federal de São Paulo (UNIFESP) Departamento de Diagnóstico por ImagemCentro de Medicina Diagnóstica FleuryUniversidade Federal de São Paulo (UNIFESP)UNIFESP, Depto. de Diagnóstico por ImagemUNIFESPSciEL

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

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