Combining super-resolution localization microscopy with pathology creates new opportunities for biomedical researches. This combination requires a suitable image mosaic method for generating a panoramic image from many overlapping super-resolution images. However, current image mosaic methods are not suitable for this purpose. Here we proposed a computational framework and developed an image mosaic method called NanoStitcher. We generated ground truth datasets and defined criteria to evaluate this computational framework. We used both simulated and experimental datasets to prove that NanoStitcher exhibits better performance than two representative image mosaic methods. This study is helpful for the mature of super-resolution digital pathology.
Recent developments in single-molecule localization microscopy (SMLM) enable researchers to study macromolecular structures at the nanometer scale. However, due to the complexity of imaging process, there are a variety of complex heterogeneous noises in SMLM data. The conventional denoising methods in SMLM can only remove a single type of noise. And, most of these denoising algorithms require manual parameter setting, which is difficult and unfriendly for biological researchers. To solve these problems, we propose a multi-step adaptive denoising framework called MSDenoiser, which incorporates multiple noise reduction algorithms and can gradually remove heterogeneous mixed noises in SMLM. In addition, this framework can adaptively learn algorithm parameters based on the localization data without manually intervention. We demonstrate the effectiveness of the proposed denoising framework on both simulated data and experimental data with different types of structures (microtubules, nuclear pore complexes and mitochondria). Experimental results show that the proposed method has better denoising effect and universality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.