Learning-based segmentation framework for tissue images containing gene expression data
Learning-based segmentation framework for tissue images containing gene expression data
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DOI：
10.1109/tmi.2007.895462
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发表时间：
2007-05-01
影响因子：
10.6
通讯作者：
Kakadiaris, Ioannis A.
中科院分区：
文献类型：
--
作者：
Bello, Musodiq;Ju, Tao;Kakadiaris, Ioannis A.
将特定的基因活性与大脑中的功能位置联系起来，可以更好地理解基因的作用。为了对哺乳动物基因组中的20 000多个基因进行这样的关联，需要可靠的自动化方法来表征与标准解剖模型相关的基因表达的分布。在本文中，我们提出了一种新的自动方法，结果在基因表达图像分割成不同的解剖区域，其中的表达可以量化，并与其他图像进行比较。我们的贡献是一种新的混合图谱，利用统计形状模型的基础上细分网格，纹理分化区域边界，解剖标志的功能，以划定基因表达图像中的解剖区域的边界。这个图谱为大脑内部数据提供了一个通用的坐标系，正被用于创建一个成年小鼠大脑基因表达模式的可搜索数据库。我们的框架对图像进行注释的速度快了四倍，与64张测试图像中的专家分割相比，空间重叠中值高达0.92。该工具旨在帮助科学家更有效地解释大规模基因表达模式。
Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the more than 20 000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our contribution is a novel hybrid atlas that utilizes a statistical shape model based on a subdivision mesh, texture differentiation at region boundaries, and features of anatomical landmarks to delineate boundaries of anatomical regions in gene expression images. This atlas, which provides a common coordinate system for internal brain data, is being used to create a searchable database of gene expression patterns in the adult mouse brain. Our framework annotates the images about four times faster and has achieved a median spatial overlap of up to 0.92 compared with expert segmentation in 64 images tested. This tool is intended to help scientists interpret large-scale gene expression patterns more efficiently.