Fluorescence imaging is used to visualize biological processes at molecular and cellular level. However, it has several limitations e.g., potential toxicity of fluorescent dyes, photobleaching and sensitivity to the environment. On the contrary, differential interference contrast (DIC) microscopes provide pseudo-3D images by enhancing contrast in unstained specimens and are harmless, and non-invasive compared to fluorescence imaging. This study proposes a massive-training artificial neural network (MTANN) scheme to generate simulated fluorescence images from DIC images. Experimental results showed that the proposed method generated fluorescence images of the proteins (sum of troponin T and vimentin) with SSIM value 0.878 which is close to the corresponding ‘gold standard’ images. Thus, it can be said that the proposed method contributes to harmless cell imaging.
Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.
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