Human papillomavirus (HPV) remains a leading cause of virus-induced cancers. Hence early detection of HPV plays a crucial role in providing timely, optimal and effective intervention before such a cancer develops. While conventional light microscopy constitutes one of inseparable tools applied for studying biological cell structures, its low resolution at ~100nm per pixel falls short of detecting HPV that typically has a size of 52 to 55nm in diameter, giving rise to visualisation of HPV and subsequent evaluation of the efficacy of anti-HPV drugs at such sub-pixel level a challenging task if not overwhelmingly. This study employs an explainable deep learning network of texture transformer (TTSR) to up sample by four folds (×4). In comparison with other super resolution approaches, TTSR appears to perform the best with PSNR and SSIM being 28.70 and 0.8778 respectively whereas 25.80/0.7910, 18.35/0.5059. 30.31/0.8013, and 28.07/0.6074 are observed for the methods of RCAN, Pix2Pix, CycleGAN, and ESRGAN respectively. Significantly, the training pairs of TTSR does not need to be precisely match between low (LR) and high resolution (HR) images since the LR and HR images, which are required by many other super resolution approaches. This work constitutes one of the first to detect HPV applying explainable deep learning network, which will lead to the real world implementation to evaluate the efficacy of the developed anti-HPV drugs.
DNA nanoballs (DNBs) are the basis of combinatorial probe anchor ligation sequencing and the subject of multiple antisense oligonucleotides delivery research. To monitor and recognize the DNBs accurately in the procedure of genome sequencing or drug delivery is essential. Here, a super-resolution method called parametric indirect microscopic imaging (PIMI) is applied to image the DNBs. By generating the necessary polarization azimuth, phase variation, and Stokes parameters through polarization modulation, the variation of point fields in a sample can be precisely recorded and used to describe how light coupling and scattering are different from point to point. Based on the Jones paraxial propagation model and the goodness of fit to the variation curve, the image resolution is no longer limited by optical diffraction after filtering off the scattering from all uncorrelated surrounding objective field points. Results show that PIMI can reveal the spatial distribution and morphology of DNBs, break the diffraction limit, and bring the resolution within 150 nm. We proved the advantages of PIMI for its super-resolving power of DNBs in a label-free, wide-field imaging manner, which opens opportunities for developing low cost, high throughput imaging tools for DNB metrology applications.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.