20 research outputs found

    The segmentation of nonsolid pulmonary nodules in CT images

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    Nonsolid nodules are a common radiographical finding in high resolution CT images of the lung. A main factor in determining a nodules malignancy status is the change in the nodule size over time. A method for automatically segmenting a nonsolid nodule from CT images is presented in this thesis. Precise image segmentation is a prerequisite for determining the volumetric growth rate from multiple image scans and the corresponding nodule malignancy status. There has been limited previous work on a segmentation technique for nonsolid nodules. The methods that have been proposed have lacked clinical validation with a radiologist ground truth and often include smaller datasets. The method in this thesis directly compares radiologist ground truth with our automated method and examines the consistency of growth measurement for further validation. The segmentation method consists of three stages; bilateral noise reduction, a probability based voxel classifier and geometric vessel removal. Parameter optimization and validation of the segmentation algorithm is facilitated with a dataset of 20 nonsolid nodule images in which a radiologist has established ground truth by outlining the boundary of the nodule in each image that it is visible. The optimal parameters were determined using the overlap metric and a training/testing methodology. The automated method achieved an average overlap of 0.43 with the radiologist ground truth. An experiment was conducted to determine whether the radiologist manual boundaries or the automated segmentations were more consistent at measuring the volumetric growth between three time scans of the same nodule. Results were determined for two different growth models (exponential and linear) on a dataset of 25 nonsolid nodules. The growth variation of the automated method was found to be 1.87 compared to the radiologist growth variation of 3.00. This suggests that, if the assumption of consistent nodule growth holds for nonsolid nodules, then the automated method provides a more precise growth rate estimate than the radiologist markings

    Automated volumetric segmentation method for computerized-diagnosis of pure nodular ground-glass opacity in high-resolution CT

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    While accurate diagnosis of pure nodular ground glass opacity (PNGGO) is important in order to reduce the number of unnecessary biopsies, computer-aided diagnosis of PNGGO is less studied than other types of pulmonary nodules (e.g., solid-type nodule). Difficulty in segmentation of GGO nodules is one of technical bottleneck in the development of CAD of GGO nodules. In this study, we propose an automated volumetric segmentation method for PNGGO using a modeling of ROI histogram with a Gaussian mixture. Our proposed method segments lungs and applies noise-filtering in the pre-processing step. And then, histogram of selected ROI is modeled as a mixture of two Gaussians representing lung parenchyma and GGO tissues. The GGO nodule is then segmented by region-growing technique that employs the histogram model as a probability density function of each pixel belonging to GGO nodule, followed by the elimination of vessel-like structure around the nodules using morphological image operations. Our results using a database of 26 cases indicate that the automated segmentation method have a promising potential

    순수 κ²°μ ˆν˜• κ°„μœ λ¦¬ 음영의 컴퓨터 보쑰진단: SVM을 μ΄μš©ν•œ μ–‘μ„± 및 μ•…μ„±λ³‘λ³€μ˜ λΆ„λ₯˜

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    The file attached is author's final draft.Lung cancer is one of the most prevalent diseases in the world. The widespread use of computed tomography (CT) for the detection of lung cancer has increased the frequency of detection of subtle nodules or ground-glass opacities (GGOs). GGOs may be observed in malignancies such as bronchioloalveolar carcinoma and adenocarcinoma as well as in their putative precursors such as atypical adenomatous hyperplasia; GGOs may also be seen in the presence of benign conditions. According to several studies of pathologically proven cases, pure nodular GGOs (PNGGOs) are found in a significant proportion of benign diseases. Hence, accurate differentiation between benign and malignant PNGGOs is important, especially in the case of lung cancer, in order to reduce unnecessary surgeries. In this study, we propose a computer-aided diagnosis (CAD) system to classify PNGGOs detected using multidetector CT images into benign or malignant categories. Our system comprises the following 3 steps: (1) automated segmentation of PNGGOs is performed using a Gaussian mixture model of the region-of-interest histogram; (2) statistical features of segmented PNGGOs regions are then extracted; and finally, (3) a support vector machine (SVM) classifier with a radial basis function kernel is applied. The experiment was performed using 35 CT volume images with 36 nodules. An exhaustive search was performed to find the best combination of features to be used as input variables for the SVM. Of the statistical features, entropy; mean; absolute deviation; skewness; kurtosis; histogram at the 25th, 50th, and 75th percentiles; and interquartile range were used as trial input variables for the exhaustive search. Entropy, kurtosis, and histogram at the 50th percentile were found to be best combination of features for use with the SVM classifier. Using leave-one-out validation, an area under the receiver operating characteristic curve of 0.91 was achieved. These results show the potential of our CAD system for differentiation between benign and malignant PNGGOs. 폐암은 μ „μ„Έκ³„μ μœΌλ‘œ μœ λ³‘μœ¨μ΄ 높은 μ§ˆν™˜μœΌλ‘œμ¨, 2008λ…„ ν†΅κ³„μ²­μ˜ μžλ£Œμ— μ˜ν•˜λ©΄ μ•” 사망원인 1μœ„μ΄λ‹€. 졜근 μ‘°κΈ° 폐암 λ°œκ²¬μ„ μœ„ν•œ CT 검사가 μž„μƒ ν˜„μž₯μ—μ„œ κ΄‘λ²”μœ„ν•˜κ²Œ μ‚¬μš©λ¨μœΌλ‘œμ¨, κ³Όκ±° 일반 X-μ„  흉뢀 μ˜μƒμ—μ„œλŠ” 보이지 μ•Šμ•˜λ˜ κ²°μ ˆν˜• κ°„μœ λ¦¬ 음영이 ν”νžˆ 발견되고 μžˆλ‹€. κ²°μ ˆν˜• κ°„μœ λ¦¬ μŒμ˜μ€ CT μ˜μƒμ—μ„œ ν˜ˆκ΄€μ„ 가리지 μ•Šκ³  λ‚˜νƒ€λ‚˜λŠ” κ°„μœ λ¦¬ ν˜•νƒœμ˜ μŒμ˜μ˜μ—­μœΌλ‘œ μΆ”μ κ²€μ‚¬μ—μ„œ μ—†μ–΄μ§€κ±°λ‚˜, μž‘μ•„μ§€μ§€ μ•ŠλŠ” κ²½μš°μ—λŠ” 세기관지 폐포성 μ„ μ•”μ΄λ‚˜, νμ„ μ•”μ˜ κ°€λŠ₯성이 맀우 λ†’λ‹€. μΆ”μ κ²€μ‚¬μ—μ„œ μ—†μ–΄μ§€κ±°λ‚˜, μž‘μ•„μ§€μ§€ μ•ŠλŠ” κ²°μ ˆν˜• κ°„μœ λ¦¬ 음영 쀑에 병변내뢀에 κ³ ν˜•μ„± 뢀뢄이 보이지 μ•ŠλŠ”, 순수 κ²°μ ˆν˜• κ°„μœ λ¦¬ μŒμ˜μ€ κ΅­μ†Œ κ°„μ§ˆμ„± μ„¬μœ μ¦μ΄λ‚˜ λΉ„μ •ν˜• μ„ μ’…μ„± 증식증 λ“±μ˜ μ–‘μ„± λ³‘λ³€μ˜ κ°€λŠ₯성이 λ†’μ•„μ„œ 이듀을 λͺ¨λ‘ μ ˆμ œν•  경우 ν™˜μžμ—κ²Œ λΆˆν•„μš”ν•œ μ ˆμ œμˆ˜μˆ μ„ μ‹œν–‰ν•  κ°€λŠ₯성이 λ†’λ‹€. ν•˜μ§€λ§Œ, 지속성 순수 κ²°μ ˆν˜• κ°„μœ λ¦¬ 음영 병변듀 μ€‘μ—μ„œ 18% ~ 48%의 병변이 μ‘°κΈ° νμ•”μ΄λž€ μ μ—μ„œ, κ²°μ½” κ°„κ³Όν•  수 μ—†λŠ” 병변이닀. κ·ΈλŸ¬λ―€λ‘œ, 순수 κ²°μ ˆν˜• κ°„μœ λ¦¬ 음영으둜 λ³΄μ΄λŠ” 병변을, μ•…μ„± 병변과 μ–‘μ„± λ³‘λ³€μœΌλ‘œ ꡬ뢄해 λ‚΄λŠ” 일이 맀우 μ€‘μš”ν•˜λ©°, 이λ₯Ό 톡해, μ–‘μ„± λ³‘λ³€μ˜ λΆˆν•„μš”ν•œ μˆ˜μˆ μ„ 막을 수 있으며, μ•…μ„± λ³‘λ³€μ˜ 경우, μ‘°κΈ° 진단 및 치료λ₯Ό 톡해, ν™˜μžμ˜ μ‚Άμ˜ 질과 생λͺ…을 μ—°μž₯ν•  수 μžˆλ‹€. λ³Έ μ—°κ΅¬μ˜ λͺ©μ μ€ 순수 κ²°μ ˆν˜• κ°„μœ λ¦¬ 음영의 μ–‘μ„±κ³Ό μ•…μ„± μ—¬λΆ€λ₯Ό νŒλ³„ν•  수 μžˆλŠ” 컴퓨터보쑰진단 μ‹œμŠ€ν…œμ˜ κ°œλ°œμ΄λ‹€. μš°λ¦¬κ°€ μ œμ•ˆν•˜λŠ” μ‹œμŠ€ν…œμ€ κ°„μœ λ¦¬ 음영 결절의 μžλ™λΆ„ν• , ν†΅κ³„κΈ°λ°˜μ˜ νŠΉμ§•λ²‘ν„°μΆ”μΆœ 그리고 Support Vector Machine (SVM) 을 μ΄μš©ν•œ λΆ„λ₯˜λͺ¨λΈλ‘œ κ΅¬μ„±λ˜μ–΄μžˆλ‹€. 35 CT λ³Όλ₯¨ μ΄λ―Έμ§€μ—μ„œ 얻은 36개의 순수 κ²°μ •ν˜• κ°„μœ λ¦¬ μŒμ˜μ— λŒ€ν•œ μ‹€ν—˜ κ²°κ³Ό leave-one-out 검증기법과 ROC뢄석을 ν†΅ν•˜μ—¬ 0.91 areas under the curve (AUC) 수치λ₯Ό μ–»μ—ˆλ‹€. SVM λΆ„λ₯˜λͺ¨λΈμ˜ μž…λ ₯으둜써, 졜적의 μ„±λŠ₯을 λ³΄μ—¬μ£ΌλŠ” νŠΉμ§•λ²‘ν„°μ˜ 쑰합은 Entropy, Kurtosis, Histogram at 75th percentile둜 λ‚˜νƒ€λ‚¬λ‹€. 이와 같은 μ‹€ν—˜κ²°κ³ΌλŠ” 순수 κ²°μ ˆν˜• κ°„μœ λ¦¬ 음영의 μ •ν™•ν•œ 악성도 진단에 μžˆμ–΄μ„œ μš°λ¦¬κ°€ μ œμ•ˆν•œ 컴퓨터보쑰진단 μ‹œμŠ€ν…œμ˜ κ°€λŠ₯성을 λ³΄μ—¬μ£Όμ—ˆκ³  ν–₯ν›„ λ°©μ‚¬μ„ κ³Όμ˜μ‚¬μ— μ˜ν•œ 진단에 λ³΄μ‘°μ‹œμŠ€ν…œμœΌλ‘œμ¨ 상보적 μ—­ν• μ˜ κ°€λŠ₯성을 λ³΄μ—¬μ£Όμ—ˆλ‹€

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

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    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

    Lung cancer screening: clinical implications

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    Lung cancer screening: clinical implications

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    Computed tomography reading strategies in lung cancer screening

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    Implementing streamlined radiology reporting and clinical results management in low-dose CT screening for lung cancer

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    Lung cancer kills more people in the UK than any other cancer. Mortality rates are poor, with fewer than 10% of people alive 10 years after diagnosis. Lung Cancer Screening (LCS) with low-dose CT (LDCT) is effective at reducing lung cancer mortality when employed in at-risk populations; because of this, in the US, LCS has been implemented as a national programme. The UK does not currently screen for lung cancer, but in 2019 NHS England announced a pilot scheme to implement lung health checks (LHC) in areas with the poorest lung cancer outcomes. Despite these advances in LCS in the UK, there are outstanding questions about how LCS could be implemented safely and effectively, which this thesis, based on experience and data from the SUMMIT Study, aims to investigate. To provide screening safely, implementation of any study or programme must focus on maintaining a favourable cost to benefit ratio. This is particularly true in LCS where high false positive and overdiagnosis rates, as well as considerable levels of incidental findings, lead to possible psychological stress, needless investigations and interventions, making provision challenging to both screenees and healthcare providers. The SUMMIT Study investigates how to deliver evidence-based LCS in a large population (25,000), and this thesis in particular focusses on how LCS can be streamlined through proformatisation of radiological data collection, clinical actioning of results and standardised communication with general practitioners (GPs) and participants. This thesis explains the approach to managing pulmonary nodules and incidental findings detected at LDCT in SUMMIT, and how these findings are collected, triaged, and communicated in a way that is both efficient and safe. Early data from SUMMIT is presented to understand how evidence-based proformas may enable streamlined clinical management, data collection and results communications, while decreasing the burden on healthcare professionals and participants alike
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