Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection.

The growing popularity of social media has engendered the social problem of spam proliferation through this medium. New spam types that evade existing spam detection systems are being developed continually, necessitating corresponding countermeasures. This study proposes an anomaly detection-based framework to detect new Twitter spam, which works by modeling the characteristics of non-spam tweets and using anomaly detection to classify tweets deviating from this model as anomalies. However, because modeling varied non-spam tweets is challenging, the technique's spam detection and false positive (FP) rates are low and high, respectively. To overcome this shortcoming, anomaly detection is performed on known spam tweets pre-detected using a trained decision tree while modeling normal tweets. A one-class support vector machine and an autoencoder with high detection rates are used for anomaly detection. The proposed framework exhibits superior detection rates for unknown spam compared to conventional techniques, while maintaining equivalent or improved detection and FP rates for known spam. Furthermore, the framework can be adapted to changes in spam conditions by adjusting the costs of detection errors.

Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising.

BACKGROUND: Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. METHOD: We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. RESULTS: Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. CONCLUSION: We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field.

Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images.

The segmentation of coronary arteries in X-ray images is essential for image-based guiding procedures and the diagnosis of cardiovascular disease. However, owing to the complex and thin structures of the coronary arteries, it is challenging to accurately segment arteries in X-ray images using only a single neural network model. Consequently, coronary artery images obtained by segmentation with a single model are often fragmented, with parts of the arteries missing. Sophisticated post-processing is then required to identify and reconnect the fragmented regions. In this paper, we propose a method to reconstruct the missing regions of coronary arteries using X-ray angiography images. METHOD: We apply an independent convolutional neural network model considering local details, as well as a local geometric prior, for reconnecting the disconnected fragments. We implemented and compared the proposed method with several convolutional neural networks with customized encoding backbones as baseline models. RESULTS: When integrated with our method, existing models improved considerably in terms of similarity with ground truth, with a mean increase of 0.330 of the Dice similarity coefficient in local regions of disconnected arteries. The method is efficient and is able to recover missing fragments in a short number of iterations. CONCLUSION AND SIGNIFICANCE: Owing to the restoration of missing fragments of coronary arteries, the proposed method enables a significant enhancement of clinical impact. The method is general and can simply be integrated into other existing methods for coronary artery segmentation.

Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images.

Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust 'vesselness' measurement is essential for extracting coronary centerlines. Importantly, we employed the DNN to robustly measure the vesselness using patch images, and we integrated softmax values to the likelihood function in our particle filtering framework. Tangent patches represent cross-sections of coronary arteries of circular shapes. Thus, 2D tangent patches are assumed to include enough features of coronary arteries, and the use of 2D patches significantly reduces computational complexity. Because coronary vasculature has multiple bifurcations, we also modeled a method to detect branching sites by clustering the particle locations. The proposed method is compared with three commercial workstations and two conventional methods from the academic literature.

Deep Reinforcement Learning with Explicit Spatio-Sequential Encoding Network for Coronary Ostia Identification in CT Images.

Accurate identification of the coronary ostia from 3D coronary computed tomography angiography (CCTA) is a essential prerequisite step for automatically tracking and segmenting three main coronary arteries. In this paper, we propose a novel deep reinforcement learning (DRL) framework to localize the two coronary ostia from 3D CCTA. An optimal action policy is determined using a fully explicit spatial-sequential encoding policy network applying 2.5D Markovian states with three past histories. The proposed network is trained using a dueling DRL framework on the CAT08 dataset. The experiment results show that our method is more efficient and accurate than the other methods. blueFloating-point operations (FLOPs) are calculated to measure computational efficiency. The result shows that there are 2.5M FLOPs on the proposed method, which is about 10 times smaller value than 3D box-based methods. In terms of accuracy, the proposed method shows that 2.22 ± 1.12 mm and 1.94 ± 0.83 errors on the left and right coronary ostia, respectively. The proposed method can be applied to the tasks to identify other target objects by changing the target locations in the ground truth data. Further, the proposed method can be utilized as a pre-processing method for coronary artery tracking methods.

Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in Swine.

PURPOSE: To compare image quality in selective intracoronary contrast-injected computed tomography angiography (Selective-CTA) with that in conventional intravenous contrast-injected CTA (IV-CTA). MATERIALS AND METHODS: Six pigs (35 to 40 kg) underwent both IV-CTA using an intravenous injection (60 mL) and Selective-CTA using an intracoronary injection (20 mL) through a guide-wire during/after percutaneous coronary intervention. Images of the common coronary artery were acquired. Scans were performed using a combined machine comprising an invasive coronary angiography suite and a 320-channel multi-slice CT scanner. Quantitative image quality parameters of CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean lumen diameter (MLD), and mean lumen area (MLA) were measured and compared. Qualitative analysis was performed using intraclass correlation coefficient (ICC), which was calculated for analysis of interobserver agreement. RESULTS: Quantitative image quality, determined by assessing the uniformity of CT attenuation (399.06 vs. 330.21, p<0.001), image noise (24.93 vs. 18.43, p<0.001), SNR (16.43 vs. 18.52, p=0.005), and CNR (11.56 vs. 13.46, p=0.002), differed significantly between IV-CTA and Selective-CTA. MLD and MLA showed no significant difference overall (2.38 vs. 2.44, p=0.068, 4.72 vs. 4.95, p=0.078). The density of contrast agent was significantly lower for selective-CTA (13.13 mg/mL) than for IV-CTA (400 mg/mL). Agreement between observers was acceptable (ICC=0.79±0.08). CONCLUSION: Our feasibility study in swine showed that compared to IV-CTA, Selective-CTA provides better image quality and requires less iodine contrast medium.

Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images.

We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.

Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System.

PURPOSE: To evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA). MATERIALS AND METHODS: We analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at ≤0.8, as well as ≤0.75, and obstructive CTA stenosis was defined that ≥50%. The diagnostic performance of vFFR was compared to invasive FFR at both ≤0.8 and ≤0.75. RESULTS: The average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI -0.011 to 0.021) with 95% limits of agreement of -0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75. CONCLUSION: Our novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.

Image-Based Flow Simulations of Pre- and Post-left Atrial Appendage Closure in the Left Atrium.

PURPOSE: For patients with atrial fibrillation, the left atrial appendage (LAA) is often the site of thrombus formation due to low atrial ejection fraction that triggers strokes and other thromboembolic events. Recently introduced percutaneous LAA occlusion procedure is known to reduce LAA-induced strokes. Despite having the procedure, there are still 11% of the patients who continue to suffer from future strokes or transient ischemic attacks, not accounting for the procedural related complications. The high failure rate is largely due to the variabilities in LAA's shape, size, and contractility which may result in ineffectiveness of this procedure. To correctly identify the candidates and evaluate the effectiveness of the procedure, we rely on patient-specific CT scans which provides the exact LA and LAA geometries and predictive hemodynamic analysis to assist in evaluating quantitative flow parameters pre- and post-LAA occlusion procedures. Hemodynamic parameters are critical to predict adverse hemodynamic flow patterns in LAA as well as the effectiveness of LAA closure in individual patient. The aim of this paper is to establish an image-based patient-specific computational fluid dynamic (CFD) simulation framework specific to the prediction of treatment outcomes of LAA closure with atrial fibrillation. This framework utilizes automated LA/LAA image segmentation which yields significant reduction in image processing. One set of patient data with successful procedure outcome is used to illustrate the potential of the proposed framework. METHODS: The proposed LAA occlusion simulation framework is composed of several components: (1) a novel image segmentation procedure, which is fully-automated to identify LA/LAA geometries from CT images, (2) a finite-element mesh generation procedure which transforms the surface geometry into a 3-D volume mesh and properly identified boundary planes, (3) performing CFD simulations with atrial fibrillation flow boundary conditions, and (4) analyzing flow characteristics (velocity, flow patterns, streamlines, vortices) within the LA for before and after LAA closure. RESULTS: Based on the LA/LAA segmentation of a 65 year old female patient with chronic atrial fibrillation, a CFD analysis was pursued to examine flow characteristics upon LAA closure. The results showed that the flow velocity magnitudes were significantly reduced by a maximum factor of 2.21, flow streamlines were greatly stabilized, and mitral outflow appeared to be more organized. Vortices were dramatically reduced in size, number, intensity, as well as duration. During diastole, the peak vortex diameter was reduced from 2.8 to 1.5 cm, while the vortex duration was reduced from 0.210 to 0.135 s. These flow characteristics all indicated a reduced risk in future thrombus formation and strokes based on the established relationship between flow and thrombus formation. For the patient case under study, the effectiveness of the procedure is predicted and found to be consistent with the actual procedural outcome. CONCLUSIONS: This framework successfully predicted patient-specific outcome of a LAA closure procedure for one patient with atrial fibrillation. It can be further developed into a useful tool for pre-procedural planning and candidate selection. More patient data are necessary for further validation studies.

Clinical feasibility of catheter-directed selective intracoronary computed tomography angiography using an extremely low dose of iodine in patients with coronary artery disease.

OBJECTIVE: This study aimed to evaluate the clinical feasibility of catheter-directed selective computed tomography angiography (S-CTA) in patients with coronary artery disease (CAD). METHODS: We prospectively enrolled 65 patients diagnosed with CAD who underwent conventional computed tomography angiography (C-CTA). C-CTA was performed with 60-90 mL of contrast medium (370 mg iodine/mL), whereas S-CTA was performed with 15 mL of contrast medium and 17.19 mg iodine/mL. Luminal enhancement range, homogeneity of luminal enhancement, image quality, plaque volume (PV), and percent aggregate plaque volume (%APV) were measured. Paired Student's t test, Wilcoxon rank-sum test, and Pearson's correlation coefficient were used to compare two methods. RESULTS: Luminal enhancement was significantly higher on S-CTA than on C-CTA (324.4 ± 8.0 Hounsfield unit (HU) vs. 312.0 ± 8.0 HU, p < 0.0001 in the per-vessel analysis). Transluminal attenuation gradient showed a significantly slower reduction pattern on S-CTA than on C-CTA (-0.65 HU/10 mm vs. -0.89 HU/10 mm, p < 0.0001 in the per-vessel analysis). Image noise was significantly lower on S-CTA than on C-CTA (39.6 ± 10.0 HU vs. 43.9 ± 9.4 HU, p < 0.0001). There was excellent correlation between S-CTA and C-CTA with respect to PV and %APV (r = 0.99, r = 0.98, respectively). CONCLUSIONS: S-CTA might be useful in facilitating atherosclerotic plaque analysis and providing guidance for complex lesions such as chronic total occlusion, particularly in cases in which on-site procedure planning is required. KEY POINTS: • Selective computed tomography angiography (S-CTA) can serve as an intraprocedural computed tomography angiography protocol. • S-CTA was performed with low dose of iodine compared with conventional computed tomography angiography. • S-CTA enables on-site atherosclerotic plaque analysis.

Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography.

We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.

Viability assessment after conventional coronary angiography using a novel cardiovascular interventional therapeutic CT system: Comparison with gross morphology in a subacute infarct swine model.

BACKGROUND: Given the lack of promptness and inevitable use of additional contrast agents, the myocardial viability imaging procedures have not been used widely for determining the need to performing revascularization. OBJECTIVE: This study is aimed to evaluate the feasibility of myocardial viability assessment, consecutively with diagnostic invasive coronary angiography (ICA) without use of additional contrast agent, using a novel hybrid system comprising ICA and multislice CT (MSCT). METHODS: In all, 14 Yucatan miniature swine models (female; age, 3 months; weight, 28-30 kg) were subjected to ICA followed by balloon occlusion (90 minutes) and reperfusion of the left anterior descending coronary artery. Two weeks after induction of myocardial infarction, delayed hyperenhancement (DHE) images were obtained, using a novel combined machine comprising ICA and 320-channel MSCT scanner (Aquilion ONE, Toshiba), after 2, 5, 7, 10, 15, and 20 minutes after conventional ICA. The heart was sliced in 10-mm consecutive sections in the short-axis plane and was embedded in a solution of 1% triphenyltetrazolium chloride (TTC). Infarct size was determined as TTC-negative areas as a percentage of total left ventricular area. On MSCT images, infarct size per slice was calculated by dividing the DHE area by the total slice area (%) and compared with histochemical analyses. RESULTS: Serial MSCT scans revealed a peak CT attenuation of the infarct area (222.5 ± 36.5 Hounsfield units) with a maximum mean difference in CT attenuation between the infarct areas and normal myocardium of at 2 minutes after contrast injection (106.4; P for difference = 0.002). Furthermore, the percentage difference of infarct size by MSCT vs histopathologic specimen was significantly lower at 2 (8.5% ± 1.8%) and 5 minutes (9.5% ± 1.9%) than those after 7 minutes. Direct comparisons of slice-matched DHE area by MSCT demonstrated excellent correlation with TTC-derived infarct size (r = 0.952; P < .001). Bland-Altman plots of the differences between DHE by MSCT and TTC-derived infarct measurements plotted against their means showed good agreement between the 2 methods. CONCLUSION: The feasibility of myocardial viability assessment by DHE using MSCT after conventional ICA was proven in experimental models, and the optimal viability images were obtained after 2 to 5 minutes after the final intracoronary injection of contrast agent for conventional ICA.

Feasibility of Selective Catheter-Directed Coronary Computed Tomography Angiography Using Ultralow-Dose Intracoronary Contrast Injection in a Swine Model.

OBJECTIVE: Selective catheter-directed intracoronary contrast injected coronary computed tomography angiography (selective CCTA) has recently been introduced for on-site evaluation of coronary artery disease during coronary artery catheterization. In this study, we aimed to develop a feasible protocol for selective CCTA using ultralow-dose contrast medium as compared with conventional intravenous CCTA (IV CCTA). MATERIALS AND METHODS: A novel combined system incorporating coronary angiography and a 320-detector row computed tomographic scanner was used to study 4 swine (35-40 kg) under animal institutional review board approval. A selective CCTA scan was simultaneously performed with an injection of 13.13 mgI/mL of modulated contrast medium at multiple different injection rates including 2, 3, and 4 mL/s and different total injection volumes of either 20 or 30 mL. Intravenous CCTA was performed with 60 mL of contrast medium, followed by 30 mL of saline chaser at 5 mL/s. Coronary mean and peak intensity, transluminal attenuation gradient, as well as 3-dimensional maximum intensity projections were obtained. RESULTS: Attenuation values (mean ± standard error, in Hounsfield units [HUs]) of selective CCTA for the left anterior descending (LAD) and right coronary artery (RCA) using the various combinations of injection rates and total injection volumes were as follows: 20 mL at 2 mL/s (LAD, 270.3 ± 20.4 HU; RCA, 322.6 ± 7.4 HU), 20 mL at 3 mL/s (LAD, 262.9 ± 20.4 HU; RCA, 264.7 ± 7.4 HU), 30 mL at 3 mL/s (LAD, 276.8 ± 20.4 HU; RCA, 274.0 ± 7.4 HU), 20 mL at 4 mL/s (LAD, 268.0 ± 20.4 HU; RCA, 277.7 ± 7.4 HU), and 30 mL at 4 mL/s (LAD, 251.3 ± 20.4 HU; RCA, 334.7 ± 7.4 HU). The representative protocol of the selective CCTA studies produced results within the optimal enhancement range (approximately 250-350 HU) for all segments, and comparison of transluminal attenuation gradient data with selective CCTA and IV CCTA studies demonstrated that the former method was more homogenous (-1.5245 and -1.7558 for LAD as well as 0.0459 and 0.0799 for RCA, respectively). Notably, the volume of iodine contrast medium used for selective CCTA was reported to be 1.09% (0.2 g) of IV CCTA (24 g). CONCLUSIONS: The current findings demonstrate the feasibility of selective CCTA using ultralow-dose intracoronary contrast injection. This technique may provide additional means of coronary evaluation in patients who may require strategic planning before a procedure using a combined modality system.

Predicting Peri-Device Leakage of Left Atrial Appendage Device Closure Using Novel Three-Dimensional Geometric CT Analysis.

BACKGROUND: After left atrial appendage (LAA) device closure, peri-device leakage into the LAA persists due to incomplete occlusion. We hypothesized that pre-procedural three-dimensional (3D) geometric analysis of the interatrial septum (IAS) and LAA orifice can predict this leakage. We investigated the predictive parameters of LAA device closure obtained from baseline cardiac computerized tomography (CT) using a novel 3D analysis system. METHODS: We conducted a retrospective study of 22 patients who underwent LAA device closure. We defined peri-device leakage as the presence of a Doppler signal inside the LAA after device deployment (group 2, n = 5) compared with patients without peri-device leakage (group 1, n = 17). Conventional parameters were measured by cardiac CT. Angles θ and φ were defined between the IAS plane and the line, linking the LAA orifice center and foramen ovale. RESULTS: Group 2 exhibited significantly better left atrial (LA) function than group 1 (p = 0.031). Pre-procedural θ was also larger in this group (41.9° vs. 52.3°, p = 0.019). The LAA cauliflower-type morphology was more common in group 2. Overall, the patients' LA reserve significantly decreased after the procedure (21.7 mm(3) vs. 17.8 mm(3), p = 0.035). However, we observed no significant interval changes in pre- and post-procedural values of θ and φ in either group (all p > 0.05). CONCLUSION: Angles between the IAS and LAA orifice might be a novel anatomical parameter for predicting peri-device leakage after LAA device closure. In addition, 3D CT analysis of the LA and LAA orifice could be used to identify clinically favorable candidates for LAA device closure.

A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.

We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA). To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices. To improve computational efficiency, an axial slice, which contains part of three main coronary arteries, is selected and regions of interest (ROIs) are extracted in the slice. Only for the voxels belonging to the ROIs, the proposed geometric feature is calculated. With the seed points, which are the centroids of the detected vessel regions, and their vessel directions, vessel tracking method can be used for artery extraction. Here a particle filtering-based tracking algorithm is tested. Using 19 clinical CCTA datasets, it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction. ROC (receiver operating characteristic) curve analysis shows the advantages of the proposed method.