Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei
Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei
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DOI：
10.1002/cyto.a.20099
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发表时间：
2005-01-01
期刊：
影响因子：
3.7
通讯作者：
Roysam, B
中科院分区：
文献类型：
--
作者：
Lin, G;Chawla, MK;Roysam, B
背景：三维共焦图像中荧光标记细胞核的自动分割对于许多研究至关重要，例如早期基因转录的时空荧光原位杂交定量。高通量和大规模研究需要高精度和自动化水平。分割错误的常见来源包括紧密聚类和细胞核碎片。以前基于区域的方法受到限制，因为它们一次执行两个核片段的合并。为了在不牺牲规模的情况下实现更高的精度，需要更复杂且计算效率更高的算法。方法：描述了一种可以同时考虑多个对象片段的基于递归树的算法。从过度分割的数据开始，它在基于对象建模的定量评分标准的指导下有效地搜索最佳合并模式。计算受到限制合并树深度的限制。结果：我们发现所提出的方法始终表现得更好，与我们之前的算法相比，其合并精度在 92% 到 100% 范围内，即使合并树深度为 3，合并精度在 75% 到 97% 的范围内变化。整体平均精度从 90% 提高到 96%，从集合中提取的一组代表性图像的计算成本大致相同。 CA1、CA3 和大鼠海马顶叶皮层区域。结论：基于分层树模型的算法显着提高了自动核分割的准确性，而无需牺牲速度。 (C) 2004 Wiley-Liss, Inc.
Background: Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous region-based methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed.Methods: A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree.Results: The proposed method was found to perform consistently better, achieving merging accuracy in the range of 92% to 100% compared with our previous algorithm, which varied in the range of 75% to 97%, even with a modest merging tree depth of 3. The overall average accuracy improved from 90% to 96%, with roughly the same computational cost for a set of representative images drawn from the CA1, CA3, and parietal cortex regions of the rat hippocampus.Conclusion: Hierarchical tree model-based algorithms significantly improve the accuracy of automated nuclear segmentation without sacrificing speed. (C) 2004 Wiley-Liss, Inc.