Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America.
Department of Biology and Biochemistry, University of Houston, TX, United States of America.
PLoS One. 2019 Jun 7;14(6):e0215843. doi: 10.1371/journal.pone.0215843. eCollection 2019.
Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly sensitive to variablility in cell size. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional contour evolution that extends previous work on fast two-dimensional snakes. 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 localization tasks when compared to existing methods on large 3D brain images.
显微镜下的细胞分割是一个具有挑战性的问题，因为细胞通常是不对称的，并且紧密堆积。成功的细胞分割算法依赖于识别种子点，并且对细胞大小的变化非常敏感。在本文中，我们提出了一种高效且高度并行的对称三维轮廓演化公式，它扩展了快速二维蛇形模型的先前工作。我们提供了一种针对 3D 图像的优化公式，以及一种在消费类图形硬件上加速计算的策略。所提出的软件利用蒙特卡罗采样方案来加快收敛速度并减少线程发散。实验结果表明，与大型 3D 脑图像上的现有方法相比，该方法在大型 2D 和 3D 细胞定位任务中提供了卓越的性能。
