基于梯度流跟踪的3D细胞核分割
3D cell nuclei segmentation based on gradient flow tracking.
作者信息
Li Gang, Liu Tianming, Tarokh Ashley, Nie Jingxin, Guo Lei, Mara Andrew, Holley Scott, Wong Stephen T C
机构信息
Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, Boston, MA, USA.
出版信息
BMC Cell Biol. 2007 Sep 4;8:40. doi: 10.1186/1471-2121-8-40.
BACKGROUND
Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.
RESULTS
Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.
CONCLUSION
The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.
背景
从三维（3D）显微图像中可靠地分割细胞核是许多生物学研究中的一项重要任务。我们提出了一种新颖的、全自动的从3D显微图像中分割细胞核的方法。它专门设计用于分割细胞核紧密相邻或相互接触的图像中的细胞核。该分割方法有三个阶段：1）梯度扩散过程，2）梯度流跟踪与分组，3）局部自适应阈值处理。
结果
提供了在合成的和原始的3D图像上的定性和定量结果，以证明所提出方法的性能和通用性。所提出方法的过分割和欠分割百分比均约为5%。与专家手动分割相比，体积重叠率始终超过90%。
结论
所提出的算法能够以合理的精度分割从3D显微镜成像获得的紧密相邻或相互接触的细胞核。
Zotero 插件