Quantitative image analysis is an important tool in understanding cell fate processes through the study of cell morphological changes in terms of size, shape, number, and orientation. In this context, this work explores systematically the main challenges involved in the quantitative analysis of fluorescence microscopy images and also proposes a new protocol while comparing its outcome with the widely used ImageJ analysis. It is important to mention that fluorescence microscopy is by far most widely used in biocompatibility analysis (observing cell fate changes) of implantable biomaterials. In this study, we employed two different image analyses toolsets: (a) the conventionally employed ImageJ software, and (b) a recently developed automated digital image analyses framework, called ImageMKS. While ImageJ offers a powerful toolset for image analyses, it requires sophisticated user expertise to design and iteratively refine the analyses workflow. This workflow primarily comprises a sequence of image transformations that typically involve de-noising and labeling of features. On the other hand, ImageMKS automates the image analyses protocol to a large extent, and thereby mitigates the influence of the user bias on the final results. This aspect is addressed using a case study of C2C12 mouse myoblast cells grown on poly(vinylidene difluoride) (PVDF) based polymeric substrates. In particular, we used a number of fluorescence microscopy images of these mouse myoblasts grown on PVDF-based nanobiocomposites under the influence of electric field. In addition to the MKS workflows requiring much less user time because of their automation, it was observed that ImageMKS workflows consistently produced more reliable results that correlated better with the previously reported experimental studies.
Keywords: ImageJ; ImageMKS; autoamted image analysis; cell morphology.
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