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自适应分割 DAPI 染色、C 带、聚集和重叠的染色体。

Adaptive Segmentation of DAPI-stained, C-banded, Aggregated and Overlapping Chromosomes.

机构信息

Nuclear Research Center Negev, Beer-Sheba, 84190, Israel.

Department of Chemical Engineering, Sami Shamoon College of Engineering, Beer-Sheva, 8410802, Israel.

出版信息

Cell Biochem Biophys. 2024 Dec;82(4):3645-3656. doi: 10.1007/s12013-024-01453-z. Epub 2024 Aug 4.

DOI:10.1007/s12013-024-01453-z
PMID:39097855
Abstract

Existing algorithms for automated segmentation of chromosomes and centromeres do not work well for condensed, C-banded and DAPI-stained chromosomes and centromeres. Overlapping and aggregation, which frequently occur in metaphase spreads, introduce additional challenges to the counting of chromosomes and centromeres in the Dicentrics Chromosome Assay (DCA). In this paper, we introduce adaptive algorithms, for segmentation of difficult metaphase spreads that include overlapping and aggregated chromosomes. In order to enhance and segment chromosomes, two optimizations are done: (1) the best algorithm among several options is automatically chosen based on predefined figures of merit, (2) the algorithm is automatically optimized with a binary search to modify its parameters to achieve predefined thresholds. These algorithms are designed to separate mildly or moderately aggregated chromosomal clusters. The clusters are segmented by skeleton junctions, reduction of the overall object thickness, and the watershed algorithm. The chromosomes are characterized by rules we establish, using minimal assumptions. Centromeres are detected by detecting bright spots on the surface of the chromosomes, and then using cluster analysis and shape and intensity profiles to identify them as centromeres. High sensitivity and specificity for chromosome and centromere detection were achieved.

摘要

现有的染色体和着丝粒自动分割算法不适用于浓缩、C 带和 DAPI 染色的染色体和着丝粒。在中期分裂中经常发生的重叠和聚集给双着丝粒染色体分析(DCA)中的染色体和着丝粒计数带来了额外的挑战。在本文中,我们引入了自适应算法,用于分割包括重叠和聚集染色体在内的困难中期分裂。为了增强和分割染色体,进行了两项优化:(1)根据预定义的优劣标准自动选择最佳算法;(2)通过二进制搜索自动优化算法,修改其参数以达到预设的阈值。这些算法旨在分离轻度或中度聚集的染色体簇。通过骨架连接、减少整体对象厚度和分水岭算法来分割簇。染色体通过我们建立的规则进行特征描述,使用最小的假设。通过检测染色体表面的亮点来检测着丝粒,然后使用聚类分析和形状和强度分布来识别它们作为着丝粒。实现了对染色体和着丝粒检测的高灵敏度和特异性。

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利用深度学习技术自动解读双着丝粒染色体分析的辐射剂量的可行性研究。
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