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Method of integrating bright field and fluorescence channels for cell image segmentation and morphological analysis using images acquired from an imaging flow cytometer (IFC)

机译:使用从成像流式细胞仪(IFC)获取的图像整合明场和荧光通道以进行细胞图像分割和形态分析的方法

摘要

A classifier engine provides cell morphology identification and cell classification in computer-automated systems, methods and diagnostic tools. The classifier engine performs multispectral segmentation of thousands of cellular images acquired by a multispectral imaging flow cytometer. As a function of imaging mode, different ones of the images provide different segmentation masks for cells and subcellular parts. Using the segmentation masks, the classifier engine iteratively optimizes model fitting of different cellular parts. The resulting improved image data has increased accuracy of location of cell parts in an image and enables detection of complex cell morphologies in the image. The classifier engine provides automated ranking and selection of most discriminative shape based features for classifying cell types.
机译:分类器引擎在计算机自​​动化的系统,方法和诊断工具中提供细胞形态识别和细胞分类。分类器引擎执行通过多光谱成像流式细胞仪获取的数千个细胞图像的多光谱分割。根据成像模式,不同的图像为细胞和亚细胞部分提供不同的分割蒙版。使用分割蒙版,分类器引擎可以迭代地优化不同单元格零件的模型拟合。所得到的改进的图像数据具有增加的图像中细胞部分的定位精度,并能够检测图像中复杂的细胞形态。分类器引擎可对大多数基于判别形状的特征进行自动排名和选择,以对细胞类型进行分类。

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