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显微镜图像中细胞核的尺寸不变检测。

Size-Invariant Detection of Cell Nuclei in Microscopy Images.

出版信息

IEEE Trans Med Imaging. 2016 Jul;35(7):1753-64. doi: 10.1109/TMI.2016.2527740. Epub 2016 Feb 11.

DOI:10.1109/TMI.2016.2527740
PMID:26886972
Abstract

Accurate detection of individual cell nuclei in microscopy images is an essential and fundamental task for many biological studies. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. Manual detection of individual cell nuclei by visual inspection is time consuming, and prone to induce subjective bias. This makes automatic detection of cell nuclei essential for large-scale, objective studies of cell cultures. Blur, clutter, bleed-through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose a new automated method for fast and robust detection of individual cell nuclei based on their radial symmetric nature in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. The main contributions are two-fold. 1) This work presents a more accurate cell nucleus detection system using the fast radial symmetry transform (FRST). 2) The proposed cell nucleus detection system is robust against most occlusions and variations in size and moderate shape deformations. We evaluate the performance of the proposed algorithm using precision/recall rates, Fβ-score and root-mean-squared distance (RMSD) and show that our algorithm provides improved detection accuracy compared to existing algorithms.

摘要

在显微镜图像中准确地检测单个细胞核是许多生物学研究的基本任务。特别是,多变量荧光显微镜用于观察培养物中细胞的不同方面。通过目视检查手动检测单个细胞核既费时又容易引起主观偏见。因此,对于大规模、客观的细胞培养研究,自动检测细胞核是必不可少的。由于模糊、杂乱、串扰、成像噪声以及具有不同大小和形状的相互接触和部分重叠的细胞核,使用图像分析自动检测单个细胞核是一项具有挑战性的任务。在本文中,我们提出了一种基于共聚焦显微镜获得的荧光原位杂交 (FISH) 图像中细胞核的径向对称性质的快速、稳健的自动检测单个细胞核的新方法。主要贡献有两个方面。1)本文使用快速径向对称变换 (FRST) 提出了一种更准确的细胞核检测系统。2)所提出的细胞核检测系统对大多数遮挡以及大小和中等形状变形的变化具有鲁棒性。我们使用精度/召回率、Fβ分数和均方根距离 (RMSD) 来评估所提出算法的性能,并表明与现有算法相比,我们的算法提供了更高的检测精度。

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