Automated Analysis and Classification of Infected Macrophages Using Bright-Field Amplitude Contrast Data.

摘要

In this paper we demonstrate that bright-field amplitude contrast image data can be used in high throughput screening (HTS) for simultaneous measurement of cell density, cell viability and general classification of individual cells into phenotypic classes such as infected or uninfected without the need to use fluorescent dyes. We present a robust image analysis pipeline where the original data is subjected to image standardization, noise reduction and, image enhancement filters and segmentation by region growing. We further show that by implementing a robust set of image enhancement and data standardization filters, it is possible to use bright-field amplitude contrast data to count and analyze the cells without requiring specimen fixation and/or use of fluorescent dyes. This work develops new, faster and less expensive, reliable imaging techniques for live cell analysis in HTS and successfully addresses a particular need for direct measurement of cell density in HTS.