Correction of uneven illumination in color microscopic image based on fully convolutional network

A deep learning-based stripe self-correction method for stitched microscopic images

Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.

A Novel Low Rank Smooth Flat-Field Correction Algorithm for Hyperspectral Microscopy Imaging

A flat-field correction method is proposed for multiple measured hyperspectral microscopy imaging in this paper. As the most crucial preprocessing process in quantitative microscopic analysis, flat-field correction solves the uneven illumination caused by vignetting in microscopic images, and guarantees the precision of spatial and spectral information in hyperspectral microscopic imaging. In order to carry out flat-field correction and extract uneven illumination among groups of hyperspectral microscopic data containing hundreds of bands simultaneously, two properties of vignetting have been exploited: i) low-rank property is reflected by little information contained in vignetting; ii) local smoothness can be observed as a gradual change in brightness of vignetting, which is typically equivalent to the sparseness in spatial frequency domain. Combining the two properties above, a novel Low Rank Smooth Flat-field Correction (LRSFC) model modified from common orthogonal basis extraction is proposed, while an optimization is solved based on alternating direction multiplier method (ADMM), obtaining a unique flat-field term with low-rank and smooth properties. Qualitative and quantitative experimental assessments indicate that LRSFC does not add extra cell texture to the extracted flat-field term, whose performance appears prior to other state-of-the-art flat-field correction methods.

Application of Intelligent Image Matching and Visual Communication in Brand Design

In this paper, from the perspective of improving the visual communication of brand design, image texture intelligent matching processing is needed, proposing a brand design texture intelligent matching method based on visual communication, constructing a brand design texture intelligent information acquisition model under visual communication, using automatic image imaging technology for texture imaging and feature segmentation of brand design, and extracting typical brand design and ethnic design language of texture histogram, texture segmentation, and automatic matching under visual communication according to histogram distribution, brand design texture information enhancement and optimization detection by regularized feature fusion method, extraction of edge contour feature points of brand design, and texture matching with the extracted edge contour feature points of decorative patterns as input statistics. The adaptive performance of texture matching for a brand design using this method is better, and the texture discrimination ability is stronger, which improves the application research of better reflecting brand design in modern visual communication design and promotes the innovative combination of traditional cultural elements and modern design.

Microvessel quantification by fully convolutional neural networks associated with type 2 inflammation in chronic rhinosinusitis

BACKGROUND

The pathogenesis of chronic rhinosinusitis (CRS) is still unclear, and little is known about angiogenesis in this disease. We utilized a fully convolutional network (FCN), which has been extensively used in image processing to study angiogenesis in CRS.

OBJECTIVE

To explore the tissue quantification of microvessels and their potential association with inflammation in CRS by using FCN to reflect the angiogenesis condition in CRS.

METHODS

For endotyping of CRS, tissue homogenates of 79 patients with CRS who had undergone functional endoscopic sinus surgery and 17 control subjects were analyzed for interferon gamma, transforming growth factor beta, interleukin (IL)-1β, IL-5, IL-6, IL-8, IL-10, IL-17, tumor necrosis factor alpha, eosinophilic cationic protein, immunoglobulin E, and Staphylococcus aureus-immunoglobulin E(SE-IgE). A total of 552 hematoxylin and eosin-stained images of 27 CRS tissue samples were used to develop an FCN, going through training, validation, and evaluation processes. An optimized FCN was applied to quantify the microvessels of tissue samples of all subjects. Correlation analysis between microvessel quantification with phenotype, endotype, clinical characteristics, and cytokine expression of CRS was carried out.

RESULTS

Quantification of microvessels in type 2 and non-type 2 CRS demonstrated considerable differences, with a higher expression in type 2 CRS. There was a strong negative correlation between the area ratio of microvessels with tissue tumor necrosis factor alpha and transforming growth factor beta levels and a mildly positive correlation with tissue IL-5 and eosinophilic cationic protein concentration.

CONCLUSION

FCN proved to facilitate the analysis of microvessels in airway tissue samples. This study elucidated the close association of angiogenesis with endotyping, suggesting that treatment aiming at antagonizing angiogenesis may assist to the therapy for the recrudescent and refractory CRS.