Geometric lightguide for near-eye light field displays.

Most near-eye displays with one fixed focal plane suffer from the vergence-accommodation conflict and cause visual discomfort to users. In contrast, light field displays can provide natural and comfortable 3D visual sensation to users without the conflict. This paper presents a near-eye light field display consisting of a geometric lightguide and a light field generator, along with a collimator to ensure the light rays propagating in the lightguide are collimated. Unlike most lightguides, which reduce thickness by employing total internal reflection that can easily generate stray light, our lightguide directly propagates light rays without total internal reflection. The partially reflective mirrors of the lightguide expand the exit pupil to achieve an eyebox of 13m m(h o r i z o n t a l)×6.5m m(v e r t i c a l) with an eye relief of 18 mm. The collimator and the light field generator, both having effective focal lengths different in the horizontal and vertical directions, are designed to provide a 40-deg diagonal field of view. The working range of the light field generator, which is 30 cm to infinity, is verified qualitatively and quantitatively by experiments. We optimize the illuminance uniformity and analyze the illuminance variation across the eyebox. Further, we minimize the ghost artifact (referring to the split-up of light fields replicated by the partially reflective mirrors) by orienting the partially reflective mirrors at slightly different angles to enhance the image quality for short-range applications such as medical surgery.

Training With Uncertain Annotations for Semantic Segmentation of Basal Cell Carcinoma From Full-Field OCT Images.

Semantic segmentation of basal cell carcinoma (BCC) from full-field optical coherence tomography (FF-OCT) images of human skin has received considerable attention in medical imaging. However, it is challenging for dermatopathologists to annotate the training data due to OCT's lack of color specificity. Very often, they are uncertain about the correctness of the annotations they made. In practice, annotations fraught with uncertainty profoundly impact the effectiveness of model training and hence the performance of BCC segmentation. To address this issue, we propose an approach to model training with uncertain annotations. The proposed approach includes a data selection strategy to mitigate the uncertainty of training data, a class expansion to consider sebaceous gland and hair follicle as additional classes to enhance the performance of BCC segmentation, and a self-supervised pre-training procedure to improve the initial weights of the segmentation model parameters. Furthermore, we develop three post-processing techniques to reduce the impact of speckle noise and image discontinuities on BCC segmentation. The mean Dice score of BCC of our model reaches 0.503±0.003, which, to the best of our knowledge, is the best performance to date for semantic segmentation of BCC from FF-OCT images.

Toward cell nuclei precision between OCT and H&E images translation using signal-to-noise ratio cycle-consistency.

Medical image-to-image translation is often difficult and of limited effectiveness due to the differences in image acquisition mechanisms and the diverse structure of biological tissues. This work presents an unpaired image translation model between in-vivo optical coherence tomography (OCT) and ex-vivo Hematoxylin and eosin (H&E) stained images without the need for image stacking, registration, post-processing, and annotation. The model can generate high-quality and highly accurate virtual medical images, and is robust and bidirectional. Our framework introduces random noise to (1) blur redundant features, (2) defend against self-adversarial attacks, (3) stabilize inverse conversion, and (4) mitigate the impact of OCT speckles. We also demonstrate that our model can be pre-trained and then fine-tuned using images from different OCT systems in just a few epochs. Qualitative and quantitative comparisons with traditional image-to-image translation models show the robustness of our proposed signal-to-noise ratio (SNR) cycle-consistency method.

Tuning the structures based on polyoxometalates from 1-D to 2-D by using different secondary organic ligands.

Six new organic-inorganic hybrid compounds based on [XM12O40](4-) (X = heteroatom, M = metal atom), namely [Cu(pic)2][H2XM12O40]·2Hapy·2apy (X = Si, M = W for , X = Ge, M = W for and X = Si, M = Mo for ), [Cu(2,2'-bpy)2][Cu(2,2'-bpy)(H2O)][Cu(pic)2]0.5[XM12O40]·nH2O (X = Si, M = Mo, n = 0.5 for , X = Ge, M = W, n = 1 for ) and [Cu(phen)(H2O)]2[Cu(pic)2][GeW12O40]·2.5H2O () (pic = deprotonated picolinic acid, apy = 2-aminopyridine, 2,2'-bpy = 2,2'-bipyridine, phen = phenanthroline), have been synthesized and characterized by IR, UV-Vis, XRD, cyclic voltammetric measurements and single crystal X-ray diffraction analysis. Single crystal X-ray analysis reveals that compounds are isomorphous and isostructural, in which each is based on [H2XM12O40](2-) and [Cu(pic)2]. Compounds and are also isomorphous and isostructural, of which the structures are more interesting than those of compounds . Both structures are constructed from [XM12O40](4-) and metal mixed-organic-ligand complexes. Compound is also constructed from Keggin ions and metal mixed-organic-ligand complexes, which are, however, thoroughly different from those of compounds and . The photodegradation properties of compounds have been analyzed. Compounds also exhibit rapid absorption properties for RhB (Rhodamine B). Detailed analysis of the photodegradation properties of compounds reveals that the molybdate POM has stronger degradation ability for RhB than the tungstate one.