Cardiovascular disease is a leading cause of death worldwide. Since adult cardiac cells are limited in their proliferation, cardiac tissue with dead or damaged cardiac cells downstream of the occluded vessel does not regenerate after myocardial infarction. The cardiac tissue is then replaced with nonfunctional fibrotic scar tissue rather than new cardiac cells, which leaves the heart weak. The limited proliferation ability of host cardiac cells has motivated investigators to research the potential cardiac regenerative ability of stem cells. Considerable progress has been made in this endeavor. However, the optimum type of stem cells along with the most suitable matrix-material and cellular microenvironmental cues are yet to be identified or agreed upon. This review presents an overview of various types of biofunctional materials and biomaterial matrices, which in combination with stem cells, have shown promises for cardiac tissue replacement and reinforcement. Engineered biomaterials also have applications in cardiac tissue engineering, in which tissue constructs are developed in vitro by combining stem cells and biomaterial scaffolds for drug screening or eventual implantation. This review highlights the benefits of using biomaterials in conjunction with stem cells to repair damaged myocardium and give a brief description of the properties of these biomaterials that make them such valuable tools to the field.
Figure 1: SkelNetOn Challenges: Example shapes and corresponding skeletons are demonstrated for the three challenge tracks in pixel (left), point (middle), and parametric domain (right).
AbstractWe present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding. We observed that unlike traditional segmentation and detection tasks, geometry understanding is still a new area for deep learning techniques. SkelNetOn aims to bring together researchers from different domains to foster learning methods on global shape understanding tasks. We aim to improve and evaluate the state-of-theart shape understanding approaches, and to serve as reference benchmarks for future research. Similar to other challenges in computer vision [6,22], SkelNetOn proposes three datasets and corresponding evaluation methodologies; all coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2019 conference. In this paper, we describe and analyze characteristics of datasets, define the evaluation criteria of the public competitions, and provide baselines for each task.Computer vision approaches have shown tremendous progress toward understanding shapes from various data formats, especially since entering the deep learning era. Although detection, recognition, and segmentation approaches achieve highly accurate results, there has been rel-arXiv:1903.09233v3 [cs.CV]
In this paper, we propose a non-rigid surface registration algorithm that estimates the correspondence uncertainty using the Markov-chain Monte Carlo (MCMC) framework. The estimated uncertainty of the inferred registration is important for many applications, such as surgical planning or missing data reconstruction. The used Metropolis-Hastings (MH) algorithm decouples the inference from modelling the posterior using a propose-and-verify scheme. The widely used random sampling strategy leads to slow convergence rates in high dimensional space. In order to overcome this limitation, we introduce an informed probabilistic proposal based on ICP that can be used within the MH algorithm. While the ICP algorithm is used in the inference algorithm, the likelihood can be chosen independently. We showcase different surface distance measures, such as the traditional Euclidean norm and the Hausdorff distance.While quantifying the uncertainty of the correspondence, we also experimentally verify that our method is more robust than the non-rigid ICP algorithm and provides more accurate surface registrations. In a reconstruction task, we show how our probabilistic framework can be used to estimate the posterior distribution of missing data without assuming a fixed point-to-point correspondence. We have made our registration framework publicly available for the community.
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