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BACKGROUND: Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. METHODS: New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case-control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. RESULTS: The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91-1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10-8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. CONCLUSION: Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD.
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Enfisema , Doença Pulmonar Obstrutiva Crônica , Enfisema Pulmonar , Humanos , Enfisema Pulmonar/diagnóstico por imagem , Enfisema Pulmonar/genética , Estudos de Casos e Controles , Aprendizado de Máquina não Supervisionado , Pulmão , Tomografia Computadorizada por Raios XRESUMO
PURPOSE: Measurement of vertebral axial rotation (VAR) is relevant for the assessment of scoliosis. Stokes method allows estimating VAR in frontal X-rays from the relative position of the pedicles and the vertebral body. This method requires identifying these landmarks for each vertebral level, which is time-consuming. In this work, a quasi-automated method for pedicle detection and VAR estimation was proposed. METHOD: A total of 149 healthy and adolescent idiopathic scoliotic (AIS) subjects were included in this retrospective study. Their frontal X-rays were collected from multiple sites and manually annotated to identify the spinal midline and pedicle positions. Then, an automated pedicle detector was developed based on image analysis, machine learning and fast manual identification of a few landmarks. VARs were calculated using the Stokes method in a validation dataset of 11 healthy (age 6-33 years) and 46 AIS subjects (age 6-16 years, Cobb 10°-46°), both from detected pedicles and those manually annotated to compare them. Sensitivity of pedicle location to the manual inputs was quantified on 20 scoliotic subjects, using 10 perturbed versions of the manual inputs. RESULTS: Pedicles centers were localized with a precision of 84% and mean difference of 1.2 ± 1.2 mm, when comparing with manual identification. Comparison of VAR values between automated and manual pedicle localization yielded a signed difference of - 0.2 ± 3.4°. The uncertainty on pedicle location was smaller than 2 mm along each image axis. CONCLUSION: The proposed method allowed calculating VAR values in frontal radiographs with minimal user intervention and robust quasi-automated pedicle localization. These slides can be retrieved under Electronic Supplementary Material.
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Interpretação de Imagem Assistida por Computador/métodos , Radiografia/métodos , Escoliose/diagnóstico por imagem , Coluna Vertebral/diagnóstico por imagem , Adolescente , Adulto , Criança , Humanos , Estudos Retrospectivos , Rotação , Adulto JovemRESUMO
This paper presents a study of the effects of scanning parameters variability when assessing glioma sizes on MRI. A database of lesions of various shapes and sizes, segmented on 3D-SPGR MRI images, was acquired on 65 patients with low-grade glioma. Simulations of large slice thickness and patient's head rotation were performed, allowing us to study their influence on two size indices: the bi-dimensional diameter product index (computed with the two largest diameters method) and the equivalent diameter index (computed with the volume segmentation method). Results show that thick slices and axial plane rotation can induce average (maximal) uncertainties on the bi-dimensional diameter product index between 32 and 6 % (150 %) for small and large tumors (size range 0.5-286 ml). The uncertainty on the equivalent diameter index, for the same categories of tumors, drops below 8 and 0.1 % (23 %). This study shows that the volume segmentation method is subject to less variability inherent to scanning conditions compared to the two largest diameters method. It also emphasizes the need for strict clinical guidelines on the replication of scanning conditions when performing MRI follow-ups on patients harboring small tumors. These implications await confirmation on a series of real patients being re-scanned with FLAIR MRI.
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Neoplasias Encefálicas/patologia , Glioma/patologia , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Neoplasias Encefálicas/cirurgia , Glioma/cirurgia , HumanosRESUMO
Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild from severe stages corresponds to a critical transition as it correlates with clinical outcomes. Deep Learning for digitized histopathology whole-slide images (WSIs) can reduce high inter- and intra-rater variability. We demonstrate a novel solution to score fibrosis severity on a retrospective cohort of 152 Sirius-Red WSIs, with fibrosis stage annotated at slide level by an expert pathologist. We exploit multiple instance learning and multiple-inferences to address the sparsity of pathological signs. We achieved an accuracy of , an F1 score of and an AUC of . These results set new state-of-the-art benchmarks for this application.
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PURPOSE: To develop an end-to-end deep learning (DL) framework to segment ventilation defects on pulmonary hyperpolarized MRI. MATERIALS AND METHODS: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease (COPD) study is a nested longitudinal case-control study in older smokers. Between February 2016 and July 2017, 56 participants (age, mean ± SD, 74 ± 8 years; 34 men) underwent same breath-hold proton (1H) and helium (3He) MRI, which were annotated for non-ventilated, hypo-ventilated, and normal-ventilated lungs. In this retrospective DL study, 820 1H and 3He slices from 42/56 (75%) participants were randomly selected for training, with the remaining 14/56 (25%) for test. Full lung masks were segmented using a traditional U-Net on 1H MRI and were imported into a cascaded U-Net, which were used to segment ventilation defects on 3He MRI. Models were trained with conventional data augmentation (DA) and generative adversarial networks (GAN)-DA. RESULTS: Conventional-DA improved 1H and 3He MRI segmentation over the non-DA model (P = 0.007 to 0.03) but GAN-DA did not yield further improvement. The cascaded U-Net improved non-ventilated lung segmentation (P < 0.005). Dice similarity coefficients (DSC) between manually and DL-segmented full lung, non-ventilated, hypo-ventilated, and normal-ventilated regions were 0.965 ± 0.010, 0.840 ± 0.057, 0.715 ± 0.175, and 0.883 ± 0.060, respectively. We observed no statistically significant difference in DCSs between participants with and without COPD (P = 0.41, 0.06, and 0.18 for non-ventilated, hypo-ventilated, and normal-ventilated regions, respectively). CONCLUSION: The proposed cascaded U-Net framework generated fully-automated segmentation of ventilation defects on 3He MRI among older smokers with and without COPD that is consistent with our reference method.
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Aterosclerose , Doença Pulmonar Obstrutiva Crônica , Idoso , Idoso de 80 Anos ou mais , Aterosclerose/diagnóstico por imagem , Estudos de Casos e Controles , Hélio , Humanos , Pulmão/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Masculino , Prótons , Doença Pulmonar Obstrutiva Crônica/diagnóstico por imagem , Estudos RetrospectivosRESUMO
OBJECTIVES: The COVID-19 pandemic instigated multiple societal and healthcare interventions with potential to affect perinatal practice. We evaluated population-level changes in preterm and full-term admissions to neonatal units, care processes and outcomes. DESIGN: Observational cohort study using the UK National Neonatal Research Database. SETTING: England and Wales. PARTICIPANTS: Admissions to National Health Service neonatal units from 2012 to 2020. MAIN OUTCOME MEASURES: Admissions by gestational age, ethnicity and Index of Multiple Deprivation, and key care processes and outcomes. METHODS: We calculated differences in numbers and rates between April and June 2020 (spring), the first 3 months of national lockdown (COVID-19 period), and December 2019-February 2020 (winter), prior to introduction of mitigation measures, and compared them with the corresponding differences in the previous 7 years. We considered the COVID-19 period highly unusual if the spring-winter difference was smaller or larger than all previous corresponding differences, and calculated the level of confidence in this conclusion. RESULTS: Marked fluctuations occurred in all measures over the 8 years with several highly unusual changes during the COVID-19 period. Total admissions fell, having risen over all previous years (COVID-19 difference: -1492; previous 7-year difference range: +100, +1617; p<0.001); full-term black admissions rose (+66; -64, +35; p<0.001) whereas Asian (-137; -14, +101; p<0.001) and white (-319; -235, +643: p<0.001) admissions fell. Transfers to higher and lower designation neonatal units increased (+129; -4, +88; p<0.001) and decreased (-47; -25, +12; p<0.001), respectively. Total preterm admissions decreased (-350; -26, +479; p<0.001). The fall in extremely preterm admissions was most marked in the two lowest socioeconomic quintiles. CONCLUSIONS: Our findings indicate substantial changes occurred in care pathways and clinical thresholds, with disproportionate effects on black ethnic groups, during the immediate COVID-19 period, and raise the intriguing possibility that non-healthcare interventions may reduce extremely preterm births.
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COVID-19 , Pandemias , Estudos de Coortes , Controle de Doenças Transmissíveis , Inglaterra/epidemiologia , Feminino , Humanos , Recém-Nascido , Gravidez , SARS-CoV-2 , Medicina Estatal , País de Gales/epidemiologiaRESUMO
BACKGROUND: Intrauterine and postnatal weight are widely regarded as biomarkers of fetal and neonatal wellbeing, but optimal weight gain following preterm birth is unknown. We aimed to describe changes over time in birthweight and postnatal weight gain in very and extremely preterm babies, in relation to major morbidity and healthy survival. METHODS: In this cohort study, we used whole-population data from the UK National Neonatal Research Database for infants below 32 weeks gestation admitted to neonatal units in England and Wales between Jan 1, 2008, and Dec 31, 2019. We used non-linear Gaussian process to estimate monthly trends, and Bayesian multilevel regression to estimate unadjusted and adjusted coefficients. We evaluated birthweight; weight change from birth to 14 days; weight at 36 weeks postmenstrual age; associated Z scores; and longitudinal weights for babies surviving to 36 weeks postmenstrual age with and without major morbidities. We adjusted birthweight for antenatal, perinatal, and demographic variables. We additionally adjusted change in weight at 14 days and weight at 36 weeks postmenstrual age, and their Z scores, for postnatal variables. FINDINGS: The cohort comprised 90 817 infants. Over the 12-year period, mean differences adjusted for antenatal, perinatal, demographic, and postnatal variables were 0 g (95% compatibility interval -7 to 7) for birthweight (-0·01 [-0·05 to 0·03] for change in associated Z score); 39 g (26 to 51) for change in weight from birth to 14 days (0·14 [0·08 to 0·19] for change in associated Z score); and 105 g (81 to 128) for weight at 36 weeks postmenstrual age (0·27 [0·21 to 0·33] for change in associated Z score). Greater weight at 36 weeks postmenstrual age was robust to additional adjustment for enteral nutritional intake. In babies surviving without major morbidity, weight velocity in all gestational age groups stabilised at around 34 weeks postmenstrual age at 16-25 g per day along parallel percentile lines. INTERPRETATION: The birthweight of very and extremely preterm babies has remained stable over 12 years. Early postnatal weight loss has decreased, and subsequent weight gain has increased, but weight at 36 weeks postmenstrual age is consistently below birth percentile. In babies without major morbidity, weight velocity follows a consistent trajectory, offering opportunity to construct novel preterm growth curves despite lack of knowledge of optimal postnatal weight gain. FUNDING: UK Medical Research Council.
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Peso ao Nascer/fisiologia , Lactente Extremamente Prematuro/crescimento & desenvolvimento , Aumento de Peso , Bases de Dados Factuais , Inglaterra , Feminino , Idade Gestacional , Humanos , Lactente , Recém-Nascido de Peso Extremamente Baixo ao Nascer/crescimento & desenvolvimento , Recém-Nascido , Estudos Longitudinais , Masculino , País de GalesRESUMO
We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in newborn care, utilising the UK National Neonatal Research Database (NNRD). We performed clustering on time-series data of daily nutritional intakes for very preterm infants born at a gestational age less than 32 weeks (n = 45,679) over a six-year period. This revealed 46 nutritional clusters heterogeneous in size, showing common interpretable clinical practices alongside rarer approaches. Nutritional clusters with similar admission profiles revealed associations between nutritional practice, geographical location and outcomes. We show how nutritional subgroups may be regarded as distinct interventions and tested for associations with measurable outcomes. We illustrate the potential for identifying relationships between nutritional practice and outcomes with two examples, discharge weight and bronchopulmonary dysplasia (BPD). We identify the well-known effect of formula milk on greater discharge weight as well as support for the plausible, but insufficiently evidenced view that human milk is protective against BPD. Our framework highlights the potential of agnostic machine learning approaches to deliver clinical practice insights and generate hypotheses using routine data.
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Lactente Extremamente Prematuro/fisiologia , Recém-Nascido de Baixo Peso/fisiologia , Unidades de Terapia Intensiva Neonatal/estatística & dados numéricos , Apoio Nutricional/estatística & dados numéricos , Padrões de Prática Médica/estatística & dados numéricos , Peso ao Nascer , Interpretação Estatística de Dados , Bases de Dados Factuais/estatística & dados numéricos , Inglaterra , Feminino , Mortalidade Hospitalar , Humanos , Fenômenos Fisiológicos da Nutrição do Lactente , Recém-Nascido , Aprendizado de Máquina , Masculino , Leite Humano , Apoio Nutricional/métodos , Mortalidade Perinatal , Resultado do Tratamento , Aumento de PesoRESUMO
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n = 317) and EMCAP (n = 22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.
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Aterosclerose , Enfisema , Doença Pulmonar Obstrutiva Crônica , Enfisema Pulmonar , Humanos , Pulmão/diagnóstico por imagem , Doença Pulmonar Obstrutiva Crônica/diagnóstico por imagem , Enfisema Pulmonar/diagnóstico por imagemRESUMO
Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer's disease (AD) classification, since neuroanatomical patterns and genetic variants are jointly critical indicators for AD diagnosis. Generative methods, designed to model common occurring patterns, could potentially advance the understanding of this disease, but have not been fully explored for AD characterization. Moreover, the introduction of a supervised component into the generative process can constrain the model for more discriminative characterization. In this study, we propose an original method based on supervised topic modeling to characterize AD from a generative perspective, yet maintaining discriminative power at differentiating disease populations. Our topic modeling jointly exploits discretized image features and categorical genetic features. Diagnostic information - cognitively normal (CN), mild cognitive impairment (MCI) and AD - is introduced as a supervision variable. Experimental results on the ADNI cohort demonstrate that our model, while achieving competitive discriminative performance, can discover topics revealing both well-known and novel neuroanatomical patterns including temporal, parietal and frontal regions; as well as associations between genetic factors and neuroanatomical patterns.
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Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/genética , Diagnóstico por Computador/métodos , Aprendizado de Máquina Supervisionado , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Feminino , Marcadores Genéticos/genética , Humanos , Imageamento por Ressonância Magnética , Masculino , NeuroimagemRESUMO
Identifying arrhythmia substrates and quantifying their heterogeneity has great potential to provide critical guidance for radio frequency ablation. However, quantitative analysis of heterogeneity on cardiac optical coherence tomography (OCT) images is lacking. In this paper, we conduct the first study on quantifying cardiac tissue heterogeneity from human OCT images. Our proposed method applies a dropout-based Monte Carlo sampling technique to measure the model uncertainty. The heterogeneity information is extracted by decoupling the intra/inter-tissue heterogeneity and tissue boundary uncertainty from the uncertainty measurement. We empirically demonstrate that our model can highlight the subtle features from OCT images, and the heterogeneity information extracted is positively correlated with the tissue heterogeneity information from corresponding histology images.
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Full quantification of regional cerebral metabolic rate of glucose (rCMRglu) with [18F]fluorodeoxy-glucose ([18F]FDG) positron emission tomography (PET) imaging requires measurement of an arterial input function (AIF) curve, which is obtained with an invasive arterial blood sampling procedure during the scan. We previously proposed a non-invasive simultaneous estimation (nSIME) method that quantifies binding of a PET radioligand by combining individual electronic health records information and a pharmacokinetic AIF (PK-AIF) model. Initially applied only to [11C]DASB data, in this study we validate nSIME for a different radioligand, [18F]FDG, adapting the algorithm to the specific distribution and metabolism of this radioligand. We evaluate the impact of the PK-AIF model, the number of [18F]FDG-specific soft constraints, and the type of predictive strategy. The accuracy of nSIME is then compared to a population-based approach. All analyses are conducted on 67 [18F]FDG PET scans with arterial blood data available for comparison. nSIME performance is optimal for [18F]FDG when using the PK-AIF model, two soft constraints, and an aggregate model to predict the soft constraint values. Higher correlation and lower Bland-Altman spread against gold standard rCMRglu values based on arterial blood measurements are observed for nSIME (r = 0.83, spread = 1.55) compared to the population-based approach (r = 0.77, spread = 2.12). nSIME provides a data-driven estimation of both amplitude and shape of the AIF curve at the individual level and potentially enables non-invasive quantification of PET data across radioligands, avoiding the need for arterial blood sampling.
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Encéfalo , Registros Eletrônicos de Saúde , Fluordesoxiglucose F18 , Tomografia por Emissão de Pósitrons/métodos , Idoso , Encéfalo/irrigação sanguínea , Encéfalo/diagnóstico por imagem , Encéfalo/metabolismo , Feminino , Fluordesoxiglucose F18/sangue , Fluordesoxiglucose F18/metabolismo , Fluordesoxiglucose F18/farmacocinética , Humanos , Masculino , Informática Médica , Pessoa de Meia-IdadeRESUMO
This paper reviews the state-of-the-art in denoising methods for biological microscopy images and introduces a new and original sparsity-based algorithm. The proposed method combines total variation (TV) spatial regularization, enhancement of low-frequency information, and aggregation of sparse estimators and is able to handle simple and complex types of noise (Gaussian, Poisson, and mixed), without any a priori model and with a single set of parameter values. An extended comparison is also presented, that evaluates the denoising performance of the thirteen (including ours) state-of-the-art denoising methods specifically designed to handle the different types of noises found in bioimaging. Quantitative and qualitative results on synthetic and real images show that the proposed method outperforms the other ones on the majority of the tested scenarios.
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Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.
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Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE) and paraseptal emphysema (PSE). Automated classification methods based on supervised learning are generally based upon the current definition of emphysema subtypes, while unsupervised learning of texture patterns enables the objective discovery of possible new radiological emphysema subtypes. In this work, we use a variant of the Latent Dirichlet Allocation (LDA) model to discover lung macroscopic patterns (LMPs) in an unsupervised way from lung regions that encode emphysematous areas. We evaluate the possible utility of the LMPs as potential novel emphysema subtypes via measuring their level of reproducibility when varying the learning set and by their ability to predict traditional radiological emphysema subtypes. Experimental results show that our algorithm can discover highly reproducible LMPs, that predict traditional emphysema subtypes.
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Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes. These texture prototypes enable automated labeling of lung volumes, and open the way to new interpretations of lung CT scans with finer subtyping of emphysema.
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Unsupervised discovery of pulmonary emphysema subtypes offers the potential for new definitions of emphysema on lung computed tomography (CT) that go beyond the standard subtypes identified on autopsy. Emphysema subtypes can be defined on CT as a variety of textures with certain spatial prevalence. However, most existing approaches for learning emphysema subtypes on CT are limited to texture features, which are sub-optimal due to the lack of spatial information. In this work, we exploit a standardized spatial mapping of the lung and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs). Our spatial mapping is demonstrated to be a powerful tool to study emphysema spatial locations over different populations. The discovered sLTPs are shown to have high reproducibility, ability to encode standard emphysema subtypes, and significant associations with clinical characteristics.
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Pulmão/diagnóstico por imagem , Enfisema Pulmonar/diagnóstico por imagem , Algoritmos , Humanos , Pulmão/patologia , Distribuição de Poisson , Doença Pulmonar Obstrutiva Crônica/diagnóstico por imagem , Enfisema Pulmonar/patologia , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Tomografia Computadorizada por Raios XRESUMO
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries. The methods were evaluated on a publicly available lung CT database of annotated emphysema subtypes. We show that enforcing sparse decompositions from texton dictionaries and unsupervised dictionary learning can achieve high classification accuracy (>90%). The flexibility of sparse-inducing models embedded either in the representation stage or dictionary learning stage has potential in providing consistency in classification performance on heterogeneous lung CT datasets with further parameter tuning.
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Enfisema Pulmonar , Algoritmos , Humanos , Pulmão , Tomografia Computadorizada por Raios XRESUMO
Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans, using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method, when compared with several types of thresholding, provides more reproducible emphysema segmentation on repeated cardiac scans, and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.