Dynamic Heart Modeling Based on a Hybrid 3D Segmentation Approach

Author(s):  
Lixu Gu
2021 ◽  
Author(s):  
Ahmed A. Sleman ◽  
Ahmed Soliman ◽  
Mohamed Elsharkawy ◽  
Guruprasad Giridharan ◽  
Mohammed Ghazal ◽  
...  

2013 ◽  
Vol 16 (2) ◽  
Author(s):  
Esmitt Ramírez J. ◽  
Pablo Temoche ◽  
Rhadamés Carmona

The representation of an image as a flow network has gained an increased interest in research for the 2D and 3D segmentation field. One of these segmentation approaches consists in applying a minimum cut algorithm to separate the image in background and foreground. The most remarkable algorithm to segment a 2D image using this approach is GrabCut. This article presents a novel segmentation of 3D image using GrabCut implemented on the GPU. We proposed a scheme where a volume dataset is used as input, instead of a 2D image. The original GrabCut algorithm is adapted to be executed on the GPU efficiently. Our algorithm is fully parallel and is optimized to run on Nvidia CUDA. Tests performed showed excellent results with different volumes, reducing the computation time and maintaining a correct separation background/foreground.


Author(s):  
A. Adam ◽  
E. Chatzilari ◽  
S. Nikolopoulos ◽  
I. Kompatsiaris

In this paper, we present a novel 3D segmentation approach operating on point clouds generated from overlapping images. The aim of the proposed hybrid approach is to effectively segment co-planar objects, by leveraging the structural information originating from the 3D point cloud and the visual information from the 2D images, without resorting to learning based procedures. More specifically, the proposed hybrid approach, H-RANSAC, is an extension of the well-known RANSAC plane-fitting algorithm, incorporating an additional consistency criterion based on the results of 2D segmentation. Our expectation that the integration of 2D data into 3D segmentation will achieve more accurate results, is validated experimentally in the domain of 3D city models. Results show that HRANSAC can successfully delineate building components like main facades and windows, and provide more accurate segmentation results compared to the typical RANSAC plane-fitting algorithm.


2019 ◽  
Vol 23 (6) ◽  
pp. 913-926
Author(s):  
Kakyom Kim ◽  
Giri Jogaratnam

Research findings on generations have been becoming useful for event organizers and destination developers over the past decades. The current study investigated generational differences in exhibition dimensions, satisfaction, and future intentions along with trip characteristics of visitors to the NASCAR Hall of Fame Exhibition event held in a medium-sized city in the southeastern region of the US. Analysis confirmed the existence of six exhibition dimensions labeled as "exhibits," "staff," "facility," "concessions," "audio tours," and "hard cards" on the event. As part of the most substantial results, there were both dissimilarities and similarities in the exhibition dimensions across four generations including "Matures," "Baby Boomers," "Generation X," and "Generation Y." Analysis also suggested significant differences in exhibition visitors' overall satisfaction, future intentions, and trip characteristics across the generations. Some useful implications are discussed for exhibition event managers and organizers.


Author(s):  
M. Peirlinck ◽  
F. Sahli Costabal ◽  
J. Yao ◽  
J. M. Guccione ◽  
S. Tripathy ◽  
...  

AbstractPrecision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.


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