Obtaining quantitative data from live cell images is the key to testing mechanistic hypotheses of molecular and cellular processes. The importance of using computer vision-based methods to accomplish this task is well recognized. However, in practice, investigators often encounter obstacles that render the application of computational image processing in cell biology far from routine. First, it is not always clear which measurements are necessary to characterize a molecular system and whether these measurements are sufficient to characterize the cellular process under investigation. Second, even when the requirements for measurements are well defined, it often is difficult to find a software tool to extract these data. It can be even more challenging to find software tools to answer specific questions raised by the hypotheses underlying the experiments. One solution is for investigators to develop their own software tools. This is feasible for some applications with the assistance of commercial and open-source software packages that support the assembly and integration of custom-designed algorithms, even for users with limited computational expertise. Another solution is for investigators to develop interdisciplinary collaborations with computer scientists. Such collaborations require close interaction between the computer scientists and experimental biologists to optimize the data acquisition and analytical procedures, which must be tightly coupled in any project applying computational analysis to biological image data. This article introduces the basic concepts that make the application of computational image processing to live cell image data successful.
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)