Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition.
Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition.
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DOI：
10.1186/s12859-015-0617-x
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发表时间：
2015-06-08
影响因子：
3
通讯作者：
Fischer SC
中科院分区：
文献类型：
--
作者：
Mathew B;Schmitz A;Muñoz-Descalzo S;Ansari N;Pampaloni F;Stelzer EH;Fischer SC
由于先进显微镜产生的大量数据，自动图像分析在现代生物学中至关重要。大多数应用需要可靠的细胞核分割。然而，在许多生物标本中，细胞核密集排列，在图像中看起来彼此接触。因此，三维细胞核分割的一个主要困难是分解明显相互接触的细胞核。目前的方法高度适应于特定的生物标本或特定的显微镜。它们不能保证同样准确的分割性能，即它们对不同数据集的鲁棒性不能保证。因此，这些方法需要对每个数据集进行详细的调整。我们提出了一种先进的三维细胞核分割算法，它是准确的和鲁棒的。我们的方法结合了局部自适应预处理和基于视线的分解，将明显接触的细胞核分离成近似凸的部分。我们用不同显微镜记录的不同标本的数据证明了我们算法的优越性能。用共聚焦和薄片荧光显微镜记录三维图像。这些标本是一个早期小鼠胚胎和两个不同的细胞球体。我们将算法的分割精度与测试图像的地面真实数据和最先进方法的结果进行了比较。分析表明，我们的方法在所有测试数据集中都是准确的（平均f值：91%），而其他方法在至少一个数据集（f值≤69%）上都失败了。此外，改进了核体积测量方法用于LoS分解。最先进的方法需要费力地调整参数值才能达到这些结果。我们的LoS算法不需要参数值调整。在一组固定的参数值下获得了准确的性能。我们开发了一种新的全自动化的三维细胞核分割方法。LoS是一种易于接近的特征，可以确保明显接触的细胞核的正确分裂，而不受其形状、大小或强度的影响。与最先进的方法相比，我们的方法表现出优越的性能，可以准确地处理各种测试图像。因此，我们的LoS方法可以很容易地应用于药物测试，发育和细胞生物学的定量评估。本文的在线版本（doi:10.1186/s12859-015-0617-x）包含补充材料，授权用户可以使用。
Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91 %) whereas the other methods each failed for at least one dataset (F-measure ≤ 69 %). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology. The online version of this article (doi:10.1186/s12859-015-0617-x) contains supplementary material, which is available to authorized users.
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影响因子：
2.9
作者：
Latorre A;Alonso-Nanclares L;Muelas S;Peña JM;Defelipe J
通讯作者：
Defelipe J
影响因子：
4.5
作者：
Gniadek, Thomas J.;Warren, Graham
通讯作者：
Warren, Graham
影响因子：
10.6
作者：
Delgado-Gonzalo, Ricard;Chenouard, Nicolas;Unser, Michael
通讯作者：
Unser, Michael
影响因子：
2.5
作者：
Asafi, Shmuel;Goren, Avi;Cohen-Or, Daniel
通讯作者：
Cohen-Or, Daniel
影响因子：
48
作者：
Eliceiri, Kevin W.;Berthold, Michael R.;Goldberg, Ilya G.;Ibanez, Luis;Manjunath, B. S.;Martone, Maryann E.;Murphy, Robert F.;Peng, Hanchuan;Plant, Anne L.;Roysam, Badrinath;Stuurmann, Nico;Swedlow, Jason R.;Tomancak, Pavel;Carpenter, Anne E.
通讯作者：
Carpenter, Anne E.