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We present PORT, a software platform for local data extraction and analysis of digital trace data. While digital trace data hold huge potential for social-scientific discovery, their most useful parts have been unattainable for scientists because of privacy concerns and prohibitive access to application programming interfaces. Recently, a workflow was introduced allowing citizens to donate their digital traces to scientists. In this workflow, citizens' digital traces are processed locally on their machines before providing informed consent to share a subset of the data with researchers. In this paper, we present the newly developed software PORT that implements the local processing part of this workflow, protecting privacy by shielding sensitive data from outside observers, including the researchers themselves. When using PORT, researchers can tailor the local processing procedure suitable to the data download package and research question. Thus, PORT enables a host of potential applications of social data science to hitherto unobtainable data.
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In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when the training data used to build the algorithm is similar to new unseen data one wishes to apply it to. It is often unknown in advance how an algorithm will perform on new unseen data, being a crucial reason for not deploying an algorithm at all. Therefore, tools are needed to measure the similarity of data sets. In this paper, we propose the Data Representativeness Criterion (DRC) to determine how representative a training data set is of a new unseen data set. We present a proof of principle, to see whether the DRC can quantify the similarity of data sets and whether the DRC relates to the performance of a supervised classification algorithm. We compared a number of magnetic resonance imaging (MRI) data sets, ranging from subtle to severe difference is acquisition parameters. Results indicate that, based on the similarity of data sets, the DRC is able to give an indication as to when the performance of a supervised classifier decreases. The strictness of the DRC can be set by the user, depending on what one considers to be an acceptable underperformance.
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Procesamiento de Imagen Asistido por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Algoritmos , Análisis de Datos , Interpretación Estadística de Datos , Humanos , Imagen por Resonancia Magnética , Prueba de Estudio Conceptual , Aprendizaje Automático SupervisadoRESUMEN
BACKGROUND: The most tedious and time-consuming task in medical additive manufacturing (AM) is image segmentation. The aim of the present study was to develop and train a convolutional neural network (CNN) for bone segmentation in computed tomography (CT) scans. METHOD: The CNN was trained with CT scans acquired using six different scanners. Standard tessellation language (STL) models of 20 patients who had previously undergone craniotomy and cranioplasty using additively manufactured skull implants served as "gold standard" models during CNN training. The CNN segmented all patient CT scans using a leave-2-out scheme. All segmented CT scans were converted into STL models and geometrically compared with the gold standard STL models. RESULTS: The CT scans segmented using the CNN demonstrated a large overlap with the gold standard segmentation and resulted in a mean Dice similarity coefficient of 0.92⯱â¯0.04. The CNN-based STL models demonstrated mean surface deviations ranging between -0.19â¯mm⯱â¯0.86â¯mm and 1.22â¯mm⯱â¯1.75â¯mm, when compared to the gold standard STL models. No major differences were observed between the mean deviations of the CNN-based STL models acquired using six different CT scanners. CONCLUSIONS: The fully-automated CNN was able to accurately segment the skull. CNNs thus offer the opportunity of removing the current prohibitive barriers of time and effort during CT image segmentation, making patient-specific AM constructs more accesible.
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Imagenología Tridimensional/métodos , Redes Neurales de la Computación , Diseño de Prótesis/métodos , Cráneo/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Algoritmos , Humanos , Prótesis e Implantes , Cráneo/patología , Cráneo/cirugíaRESUMEN
Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T1-weighted image, a T2-weighted fluid attenuated inversion recovery (FLAIR) image and a T1-weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge (n = 20), quantitatively and qualitatively in relatively healthy older subjects (n = 96), and qualitatively in patients from a memory clinic (n = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts.
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Encéfalo/diagnóstico por imagen , Encéfalo/patología , Imagen por Resonancia Magnética/métodos , Redes Neurales de la Computación , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología , Anciano , Artefactos , Encéfalo/irrigación sanguínea , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Masculino , Reconocimiento de Normas Patrones Automatizadas , Reproducibilidad de los Resultados , Sustancia Blanca/irrigación sanguíneaRESUMEN
INTRODUCTION: Pooling of multicenter brain imaging data is a trend in studies on ageing related brain diseases. This poses challenges to MR-based brain segmentation. The performance across different field strengths of three widely used automated methods for brain volume measurements was assessed in the present study. METHODS: Ten subjects (mean age: 64 years) were scanned on 1.5T and 3T MRI on the same day. We determined robustness across field strength (i.e., whether measured volumes between 3T and 1.5T scans in the same subjects were similar) for SPM12, Freesurfer 5.3.0 and FSL 5.0.7. As a frame of reference, 3T MRI scans from 20 additional subjects (mean age: 71 years) were segmented manually to determine accuracy of the methods (i.e., whether measured volumes corresponded with expert-defined volumes). RESULTS: Total brain volume (TBV) measurements were robust across field strength for Freesurfer and FSL (mean absolute difference as % of mean volume ≤ 1%), but less so for SPM (4%). Gray matter (GM) and white matter (WM) volume measurements were robust for Freesurfer (1%; 2%) and FSL (2%; 3%) but less so for SPM (5%; 4%). For intracranial volume (ICV), SPM was more robust (2%) than FSL (3%) and Freesurfer (9%). TBV measurements were accurate for SPM and FSL, but less so for Freesurfer. For GM volume, SPM was accurate, but accuracy was lower for Freesurfer and FSL. For WM volume, Freesurfer was accurate, but SPM and FSL were less accurate. For ICV, FSL was accurate, while SPM and Freesurfer were less accurate. CONCLUSION: Brain volumes and ICV could be measured quite robustly in scans acquired at different field strengths, but performance of the methods varied depending on the assessed compartment (e.g., TBV or ICV). Selection of an appropriate method in multicenter brain imaging studies therefore depends on the compartment of interest.
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Encéfalo/anatomía & histología , Imagen por Resonancia Magnética , Adulto , Anciano , Anciano de 80 o más Años , Encéfalo/patología , Sustancia Gris , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia Magnética/métodos , Persona de Mediana Edad , Neuroimagen , Tamaño de los Órganos , Reproducibilidad de los Resultados , Sustancia BlancaRESUMEN
BACKGROUND: The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in magnetic resonance imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary. NEW METHOD: We propose a segmentation framework based on a Conditional Random Field for brain tissue segmentation, with a Random Forest encoding the likelihood function. The features include intensities, gradients, probability maps, and locations. Additionally, skull stripping is critical for achieving an accurate segmentation; thus, after extracting the brain we propose to refine its boundary during segmentation. RESULTS: The proposed framework was evaluated on the MR Brain Image Segmentation Challenge and the Internet Brain Segmentation Repository databases. The segmentations of brain tissues obtained with the proposed algorithm were competitive both in normal and diseased subjects. The skull stripping refinement significantly improved the results, when comparing against no refinement. COMPARISON WITH EXISTING METHODS: In the MR Brain Image Segmentation Challenge database, the results were competitive when comparing with top methods. In the Internet Brain Segmentation Repository database, the proposed approach outperformed other well-established algorithms. CONCLUSIONS: The combination of a Random Forest and Conditional Random Field for brain tissue segmentation performed well for normal and diseased subjects. Additionally, refinement of the skull stripping at segmentation time is feasible in learning-based methods and significantly improves the segmentation of cerebrospinal fluid and intracranial volume.
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Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Anciano , Humanos , Cráneo/diagnóstico por imagenRESUMEN
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2-weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.
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Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Aprendizaje Automático , Imagen por Resonancia Magnética/métodos , Redes Neurales de la Computación , Adulto , Anciano , Humanos , Recién Nacido , Recien Nacido Prematuro , Adulto JovenRESUMEN
Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
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Encéfalo/anatomía & histología , Encéfalo/fisiología , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Anciano , Anciano de 80 o más Años , Algoritmos , Líquido Cefalorraquídeo/fisiología , Bases de Datos Factuales , Femenino , Sustancia Gris/anatomía & histología , Sustancia Gris/fisiología , Humanos , Masculino , Sistemas en Línea , Estándares de Referencia , Reproducibilidad de los Resultados , Programas Informáticos , Sustancia Blanca/anatomía & histología , Sustancia Blanca/fisiologíaRESUMEN
Background and Purposes. The 320-detector row CT scanner enables visualization of whole-brain hemodynamic information (dynamic CT angiography (CTA) derived from CT perfusion scans). However, arterial image quality in dynamic CTA (dCTA) is inferior to arterial image quality in standard CTA. This study evaluates whether the arterial image quality can be improved by using a total bolus extraction (ToBE) method. Materials and Methods. DCTAs of 15 patients, who presented with signs of acute cerebral ischemia, were derived from 320-slice CT perfusion scans using both the standard subtraction method and the proposed ToBE method. Two neurointerventionalists blinded to the scan type scored the arterial image quality on a 5-point scale in the 4D dCTAs in consensus. Arteries were divided into four categories: (I) large extradural, (II) intradural (large, medium, and small), (III) communicating arteries, and (IV) cerebellar and ophthalmic arteries. Results. Quality of extradural and intradural arteries was significantly higher in the ToBE dCTAs than in the standard dCTAs (extradural P = 0.001, large intradural P < 0.001, medium intradural P < 0.001, and small intradural P < 0.001). Conclusion. The 4D dCTAs derived with the total bolus extraction (ToBE) method provide hemodynamic information combined with improved arterial image quality as compared to standard 4D dCTAs.
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Encéfalo/diagnóstico por imagen , Angiografía Cerebral/métodos , Accidente Cerebrovascular/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Anciano , Anciano de 80 o más Años , Encéfalo/irrigación sanguínea , Medios de Contraste , Femenino , Hemodinámica , Humanos , Masculino , Persona de Mediana Edad , Accidente Cerebrovascular/patologíaRESUMEN
PURPOSE: To suggest a simple and robust technique used to reconstruct high-quality computed tomographic (CT) angiographic images from CT perfusion data and to compare it with currently used CT angiography techniques. MATERIALS AND METHODS: Institutional review board approval was waived for this retrospective study, which included 25 consecutive patients who had had a stroke. Temporal maximum intensity projection (tMIP) CT angiographic images were created by using prior temporal filtering as a timing-insensitive technique to produce CT angiographic images from CT perfusion data. The temporal filter strength was optimized to gain maximal contrast-to-noise ratios (CNRs) in the circle of Willis. The resulting timing-invariant (TI) CT angiography was compared with standard helical CT angiography, the arterial phase of dynamic CT angiography, and nonfiltered tMIP CT angiography. Vascular contrast, image noise, and CNR were measured. Four experienced observers scored all images for vascular noise, vascular contour, detail of small and medium arteries, venous superimposition, and overall image quality in a blinded side-by-side comparison. Measurements were compared with a paired t test; P ≤ .05 indicated a significant difference. RESULTS: On average, optimized temporal filtering in TI CT angiography increased CNR by 18% and decreased image noise by 18% at the expense of a decrease in vascular contrast of 3% when compared with nonfiltered tMIP CT angiography. CNR, image noise, vascular noise, vascular contour, detail visibility of small and medium arteries, and overall image quality of TI CT angiograms were superior to those of standard CT angiography, tMIP CT angiography, and the arterial phase of dynamic CT angiography at a vascular contrast that was similar to that of standard CT angiography. Venous superimposition was similar for all techniques. Image quality of the arterial phase of dynamic CT angiography was rated inferior to that of standard CT angiography. CONCLUSION: TI CT angiographic images constructed by using temporally filtered tMIP CT angiographic data have excellent image quality that is superior to that achieved with currently used techniques, but they suffer from modest venous superimposition.
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Angiografía Cerebral/métodos , Trastornos Cerebrovasculares/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Adulto , Medios de Contraste , Femenino , Humanos , Yohexol/análogos & derivados , Masculino , Persona de Mediana Edad , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Estudios RetrospectivosRESUMEN
Cerebral computed tomography perfusion (CTP) scans are acquired to detect areas of abnormal perfusion in patients with cerebrovascular diseases. These 4D CTP scans consist of multiple sequential 3D CT scans over time. Therefore, to reduce radiation exposure to the patient, the amount of x-ray radiation that can be used per sequential scan is limited, which results in a high level of noise. To detect areas of abnormal perfusion, perfusion parameters are derived from the CTP data, such as the cerebral blood flow (CBF). Algorithms to determine perfusion parameters, especially singular value decomposition, are very sensitive to noise. Therefore, noise reduction is an important preprocessing step for CTP analysis. In this paper, we propose a time-intensity profile similarity (TIPS) bilateral filter to reduce noise in 4D CTP scans, while preserving the time-intensity profiles (fourth dimension) that are essential for determining the perfusion parameters. The proposed TIPS bilateral filter is compared to standard Gaussian filtering, and 4D and 3D (applied separately to each sequential scan) bilateral filtering on both phantom and patient data. Results on the phantom data show that the TIPS bilateral filter is best able to approach the ground truth (noise-free phantom), compared to the other filtering methods (lowest root mean square error). An observer study is performed using CBF maps derived from fifteen CTP scans of acute stroke patients filtered with standard Gaussian, 3D, 4D and TIPS bilateral filtering. These CBF maps were blindly presented to two observers that indicated which map they preferred for (1) gray/white matter differentiation, (2) detectability of infarcted area and (3) overall image quality. Based on these results, the TIPS bilateral filter ranked best and its CBF maps were scored to have the best overall image quality in 100% of the cases by both observers. Furthermore, quantitative CBF and cerebral blood volume values in both the phantom and the patient data showed that the TIPS bilateral filter resulted in realistic mean values with a smaller standard deviation than the other evaluated filters and higher contrast-to-noise ratios. Therefore, applying the proposed TIPS bilateral filtering method to 4D CTP data produces higher quality CBF maps than applying the standard Gaussian, 3D bilateral or 4D bilateral filter. Furthermore, the TIPS bilateral filter is computationally faster than both the 3D and 4D bilateral filters.
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Circulación Cerebrovascular , Tomografía Computarizada Cuatridimensional/métodos , Encéfalo/irrigación sanguínea , Encéfalo/diagnóstico por imagen , Humanos , Fantasmas de Imagen , Control de Calidad , Factores de TiempoRESUMEN
PURPOSE: CT angiography (CTA) scans are the current standard for vascular analysis of patients with cerebrovascular diseases, such as acute stroke and subarachnoid hemorrhage. An additional CT perfusion (CTP) scan is acquired of these patients to assess the perfusion of the cerebral tissue. The aim of this study is to extend the diagnostic yield of the CTP scans to also include vascular information. METHODS: CTP scans are acquired by injecting contrast material and repeatedly scanning the head over time. Therefore, time-intensity profiles are available for each voxel in the scanned volume, resulting in a 4D dataset. These profiles can be utilized to differentiate not only between vessels and background but also between arteries and veins. In this article, a fully automatic method is proposed for the segmentation of the intracranial arteries and veins from 4D cerebral CTP scans. Furthermore, a vessel enhanced volume is presented, in which the vasculature is highlighted and background structures are suppressed. Combining this volume with the artery/vein segmentation results in an arteriogram and a venogram, which could serve as additional means for vascular analysis in patients with cerebrovascular diseases. The artery/vein segmentation is quantitatively evaluated by comparing the results to manual segmentations by two expert observers. RESULTS: Results (paired two-tailed t-test) show that the accuracies of the proposed artery/vein labeling are not significantly different from the accuracies of the expert observer manual labeling (ground truth). Moreover, sensitivity and specificity of the proposed artery/vein labeling, relative to both expert observer ground truths, were similar to the sensitivity and specificity of the expert observer labeling compared to each other. CONCLUSIONS: The proposed method for artery/vein segmentation is shown to be very accurate for arteries and veins with normal perfusion. Combining the artery/vein segmentation with the vessel enhanced volume produces an arteriogram and a venogram, which have the potential to extend the diagnostic yield of CTP scans and replace the additional CTA scan, but could also be helpful to radiologists in addition to the CTA scan.
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Angiografía Cerebral/métodos , Arterias Cerebrales/diagnóstico por imagen , Imagenología Tridimensional/métodos , Imagen de Perfusión/métodos , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Técnica de Sustracción , Tomografía Computarizada por Rayos X/métodos , Algoritmos , Inteligencia Artificial , Análisis por Conglomerados , Humanos , Reconocimiento de Normas Patrones Automatizadas/métodos , Intensificación de Imagen Radiográfica/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Noise filtering techniques that maintain image contrast while decreasing image noise have the potential to optimize the quality of computed tomography (CT) images acquired at reduced radiation dose. In this paper, a hybrid diffusion filter with continuous switch (HDCS) is introduced, which exploits the benefits of three-dimensional edge-enhancing diffusion (EED) and coherence-enhancing diffusion (CED). Noise is filtered, while edges, tubular structures, and small spherical structures are preserved. From ten high dose thorax CT scans, acquired at clinical doses, ultra low dose ( 15 mAs ) scans were simulated and used to evaluate and compare HDCS to other diffusion filters, such as regularized Perona-Malik diffusion and EED. Quantitative results show that the HDCS filter outperforms the other filters in restoring the high dose CT scan from the corresponding simulated low dose scan. A qualitative evaluation was performed on filtered real low dose CT thorax scans. An expert observer scored artifacts as well as fine structures and was asked to choose one of three scans (two filtered (blinded), one unfiltered) for three different settings (trachea, lung, and mediastinal). Overall, the HDCS filtered scan was chosen most often.