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Aims: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT)., Methods and Results: We studied 1106 HF patients from the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) (left ventricular ejection fraction ≤ 30%, QRS ≥ 130 ms, New York Heart Association class ≤ II) randomized to CRT with a defibrillator (CRT-D, n = 677) or an implantable cardioverter defibrillator (ICD, n = 429). An unsupervised ML algorithm (Multiple Kernel Learning and K-means clustering) was used to categorize subjects by similarities in clinical parameters, and left ventricular volume and deformation traces at baseline into mutually exclusive groups. The treatment effect of CRT-D on the primary outcome (all-cause death or HF event) and on volume response was compared among these groups. Our analysis identified four phenogroups, significantly different in the majority of baseline clinical characteristics, biomarker values, measures of left and right ventricular structure and function and the primary outcome occurrence. Two phenogroups included a higher proportion of known clinical characteristics predictive of CRT response, and were associated with a substantially better treatment effect of CRT-D on the primary outcome [hazard ratio (HR) 0.35; 95% confidence interval (CI) 0.19-0.64; P = 0.0005 and HR 0.36; 95% CI 0.19-0.68; P = 0.001] than observed in the other groups (interaction P = 0.02)., Conclusions: Our results serve as a proof-of-concept that, by integrating clinical parameters and full heart cycle imaging data, unsupervised ML can provide a clinically meaningful classification of a phenotypically heterogeneous HF cohort and might aid in optimizing the rate of responders to specific therapies., (© 2018 The Authors. European Journal of Heart Failure © 2018 European Society of Cardiology.)

Reparameterization of surfaces is a widely used tool in computer graphics known mostly from the remeshing algorithms. Recently, the surface reparameterization techniques started to gain popularity in the field of medical imaging, but mostly for convenient 2D visualization of the information initially represented on 3D surfaces (e.g. continuous bulls-eye plot). However, by consistently mapping the 3D information to the same 2D domain, surface reparameterization techniques allow us to put into correspondence anatomical shapes of inherently different geometry. In this paper, we propose a method for anatomical parameterization of cardiac ventricular anatomies that include myocardium, trabeculations, tendons and papillary muscles. The proposed method utilizes a quasi-conformal flattening of the myocardial surfaces of the left and right cardiac ventricles and extending it to cover the interior of the cavities using the local coordinates given by the solution of the Laplace's equation. Subsequently, we define a geometry independent representation for the detailed cardiac left and right ventricular anatomies that can be used for convenient visualization and statistical analysis of the trabeculations in a population. Lastly we show how it can be used for mapping the detailed cardiac anatomy between different hearts, which is of considerable interest for detailed cardiac computational models or shape atlases., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Integration of electrical and structural information for scar characterization in the left ventricle (LV) is a crucial step to better guide radio-frequency ablation therapies, which are usually performed in complex ventricular tachycardia (VT) cases. This integration requires finding a common representation where to map the electrical information from the electro-anatomical map (EAM) surfaces and tissue viability information from delay-enhancement magnetic resonance images (DE-MRI). However, the development of a consistent integration method is still an open problem due to the lack of a proper evaluation framework to assess its accuracy. In this paper we present both: (i) an evaluation framework to assess the accuracy of EAM and imaging integration strategies with simulated EAM data and a set of global and local measures; and (ii) a new integration methodology based on a planar disk representation where the LV surface meshes are quasi-conformally mapped (QCM) by flattening, allowing for simultaneous visualization and joint analysis of the multi-modal data. The developed evaluation framework was applied to estimate the accuracy of the QCM-based integration strategy on a benchmark dataset of 128 synthetically generated ground-truth cases presenting different scar configurations and EAM characteristics. The obtained results demonstrate a significant reduction in global overlap errors (50-100%) with respect to state-of-the-art integration techniques, also better preserving the local topology of small structures such as conduction channels in scars. Data from seventeen VT patients were also used to study the feasibility of the QCM technique in a clinical setting, consistently outperforming the alternative integration techniques in the presence of sparse and noisy clinical data. The proposed evaluation framework has allowed a rigorous comparison of different EAM and imaging data integration strategies, providing useful information to better guide clinical practice in complex cardiac interventions., (Copyright © 2016 Elsevier B.V. All rights reserved.)

This paper presents a framework for weighted fusion of several Active Shape and Active Appearance Models. The approach is based on the eigenspace fusion method proposed by Hall et al., which has been extended to fuse more than two weighted eigenspaces using unbiased mean and covariance matrix estimates. To evaluate the performance of fusion, a comparative assessment on segmentation precision as well as facial verification tests are performed using the AR, EQUINOX, and XM2VTS databases. Based on the results, it is concluded that the fusion is useful when the model needs to be updated online or when the original observations are absent.

Abstract: This paper demonstrates how a weighted fusion of multiple Active Shape (ASM) or Active Appearance (AAM) models can be utilized to perform multi-view facial segmentation with only a limited number of views available for training the models. The idea is to construct models only from frontal and profile views and subsequently fuse these models with adequate weights to segment any facial view. This reduces the problem of multi-view facial segmentation to that of weight estimation, the algorithm for which is proposed as well. The evaluation is performed on a set of 280 landmarked static face images corresponding to seven different rotation angles and on several video sequences of the AV@CAR database. The evaluation demonstrates that the estimation of the weights does not have to be very accurate in the case of ASM, while in the case of AAM the influence of correct weight estimation is more critical. The segmentation with the proposed weight estimation method produced accurate segmentations in 91% of 280 testing images with the median point-to-point error varying from two to eight pixels (1.8–7.2% of average inter-eye distance). [Copyright &y& Elsevier]