Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi

Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

Background Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists. Objectives We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist. Methods We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions. Results In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53–0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65–0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts. Conclusion This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis

Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi’s vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy

n- исчисление – реалистичная формализация класса переписывающих систем

Предложен новый формализм типизированного η-исчисления в качестве теоретической основы для по-строения специальных классов систем программирования на основе переписывающих правил. Форма-лизм использует упорядоченные неконфлюэнтные множества правил переписывания и взаимодействие с программным окружением, что позволяет расширить возможности программирования динамических приложений

Accuracy Of Whole Slide Imaging Stack Alignment In Consecutive Sections Of The Carotid Artery

Introduction/ Background Atherosclerosis is a chronic inflammatory disease of middle-sized and large arteries, characterized by the accumulation of inflammatory cells, especially mac- rophages [1] . A detailed visualization of the presence and distribution of macrophages in the atherosclerotic plaque contributes to a better understanding of the pathogenesis of atherosclerosis and the onset of acute coronary syndromes after atherosclerotic plaque rup- ture. Three-dimensional (3D) reconstruction of histology sections has the potential to improve both the detection of lesions as well as understanding in plaque growth and destabilization. Aims The objective of this study is to implement a image marker independent 3D histology reconstruction method in order to visualize the arteriosclerotic vessel and evaluate its accuracy. Methods A dataset comprising 48 consecutive cross-sections with a slice thickness of 10µm of a formalin-fixed paraf- fin-embedded (FFPE) carotid artery was used. The slideswere double stained with monoclonal antibodies and were scanned with anOlympusdotSlide scanner with a 10x objective leading to 0.65 micron pixel size. In these images, the smooth muscle cells and macrophages were visualized in blue and red, respectively. Rigid, rigid & affine, and rigid & affine & b-spline (non-rigid) automatic stack alignment was performed using elastix, an open-source toolbox for alignment of images [2]. As a consequence of the image deformation in non-rigid approaches, the diagnostic accuracy might be hindered. Therefore a small bending energy, i.e., sum of the spatial second-order derivatives of the transformation, was al- lowed. In order to increase processing speed, the stack alignment was performed on downsampled data.   An automatically determined mask of the vessel was used for pair-wise reconstruction of the vessel with re- spect to the middle slide that was chosen as a reference section. Accuracy was visually assessed using a surface plot of the lumen of the vessel. In addition, the Dice similarity coefficient, which is a measure of spatial image overlap, of consecutive pairs of slides was calculated for the different stack alignment approaches. Results Visual assessment of the surface plot of the vessels’ lu- men after pair-wise stack alignment, showed a relatively smooth surface of the lumen. This was the case for the rigid (i.e. translation and rotation), rigid & affine, and rigid & affine & b-spline approaches.  The Dice similarity coefficient of the registered masks increased with each additional alignment step. Slides alignment using rigid, rigid & affine and rigid & affine & b-spline approaches resulted in average Dice similarity coefficient of 0.85, 0.87, and 0.98, respectively. A more accurate result of the alignment comes at the cost of an increase in computation time by roughly a factor of two in each additional alignment step

Stroke Etiology and Thrombus Computed Tomography Characteristics in Patients With Acute Ischemic Stroke:A MR CLEAN Registry Substudy

Background and Purpose - If a relationship between stroke etiology and thrombus computed tomography characteristics exists, assessing these characteristics in clinical practice could serve as a useful additional diagnostic tool for the identification of stroke subtype. Our purpose was to study the association of stroke etiology and thrombus computed tomography characteristics in patients with acute ischemic stroke due to a large vessel occlusion. Methods - For 1429 consecutive patients enrolled in the MR CLEAN Registry, we determined stroke cause as defined by the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria. The association of stroke etiology with the hyperdense artery sign, clot burden score, and thrombus location was estimated with univariable and multivariable binary and ordinal logistic regression. Additionally, for 367 patients with available thin-section imaging, we assessed the association of stroke etiology with absolute and relative thrombus attenuation, distance from internal carotid artery-terminus to thrombus, thrombus length, and thrombus attenuation increase with univariable and multivariable linear regression. Results - Compared with cardioembolic strokes, noncardioembolic strokes were associated with presence of hyperdense artery sign (odds ratio, 2.2 [95% CI, 1.6-3.0]), lower clot burden score (common odds ratio, 0.4 [95% CI, 0.3-0.6]), shift towards a more proximal thrombus location (common odds ratio, 0.2 [95% CI, 0.2-0.3]), higher absolute thrombus attenuation (β, 3.6 [95% CI, 0.9-6.4]), decrease in distance from the ICA-terminus (β, -5.7 [95% CI, -8.3 to -3.0]), and longer thrombi (β, 8.6 [95% CI, 6.5-10.7]), based on univariable analysis. Thrombus characteristics of strokes with undetermined cause were similar to those of cardioembolic strokes. Conclusions - Thrombus computed tomography characteristics of cardioembolic stroke are distinct from those of noncardioembolic stroke. Additionally, our study supports the general hypothesis that many cryptogenic strokes have a cardioembolic cause. Further research should focus on the use of thrombus computed tomography characteristics as a diagnostic tool for stroke cause in clinical practice

Follow-up infarct volume as a mediator of endovascular treatment effect on functional outcome in ischaemic stroke

Objective: The putative mechanism for the favourable effect of endovascular treatment (EVT) on functional outcome after acute ischaemic stroke is preventing follow-up infarct volume (FIV) progression. We aimed to assess to what extent difference in FIV explains the effect of EVT on functional outcome in a randomised trial of EVT versus no EVT (MR CLEAN). Methods: FIV was assessed on non-contrast CT scan 5–7 days after stroke. Functional outcome was the score on the modified Rankin Scale at 3 months. We tested the causal pathway from intervention, via FIV to functional outcome with a mediation model, using linear and ordinal regression, adjusted for relevant baseline covariates, including stroke severity. Explained effect was assessed by taking the ratio of the log odds ratios of treatment with and without adjustment for FIV. Results: Of the 500 patients included in MR CLEAN, 60 died and four patients underwent hemicraniectomy before FIV was assessed, leaving 436 patients for analysis. Patients in the intervention group had better functional outcomes (adjusted common odds ratio (acOR) 2.30 (95% CI 1.62–3.26) than controls and smaller FIV (median 53 vs. 81 ml) (difference 28 ml; 95% CI 13–41). Smaller FIV was associated with better outcome (acOR per 10 ml 0.60, 95% CI 0.52–0.68). After adjustment for FIV the effect of intervention on functional outcome decreased but remained substantial (acOR 2.05, 95% CI 1.44–2.91). This implies that preventing FIV progression explains 14% (95% CI 0–34) of the beneficial effect of EVT on outcome. Conclusion: The effect of EVT on FIV explains only part of the treatment effect on functional outcome. Key Points: • Endovascular treatment in acute ischaemic stroke patients prevents progression of follow-up infarct volume on non-contrast CT at 5–7 days.• Follow-up infarct volume was related to functional outcome, but only explained a modest part of the effect of intervention on functional outcome.• A large proportion of treatment effect on functional outcome remains unexplained, suggesting FIV alone cannot be used as an early surrogate imaging marker of functional outcome