摘要翻译：
细胞分割在显微镜中是一个具有挑战性的问题，因为细胞通常是不对称的和密集的。由于人工干预和处理时间会使分割变得困难，因此对于超大图像来说，这变得特别具有挑战性。在本文中，我们提出了一个高效且高度并行的对称三维(3D)轮廓演化公式，扩展了以前关于快速二维活动轮廓的工作。我们提供了一个3D图像优化的公式，以及一个在消费者图形硬件上加速计算的策略。该软件利用Monte-Carlo采样方案，以加快收敛速度，减少线程发散。实验结果表明，与现有的三维脑图像分割方法相比，该方法在二维和三维大细胞分割任务中具有更好的性能。
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英文标题：
《Three-Dimensional GPU-Accelerated Active Contours for Automated
  Localization of Cells in Large Images》
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作者：
Mahsa Lotfollahi, Sebastian Berisha, Leila Saadatifard, Laura Montier,
  Jokubas Ziburkus, David Mayerich
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images.
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PDF链接：
https://arxiv.org/pdf/1804.06304