Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation

We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot. Classical subspace-based methods like MUSIC and ESPRIT use spatial smoothing on uniform linear arrays for single snapshot DOA estimation but face drawbacks in reduced array aperture and inapplicability to sparse arrays. Single-snapshot methods such as compressive sensing and iterative adaptation approach (IAA) encounter challenges with high computational costs and slow convergence, hampering real-time use. Recent deep learning DOA methods offer promising accuracy and speed. However, the practical deployment of deep networks is hindered by their black-box nature. To address this, we propose a deep-MPDR network translating minimum power distortionless response (MPDR)-type beamformer into deep learning, enhancing generalization and efficiency. Comprehensive experiments conducted using both simulated and real-world datasets substantiate its dominance in terms of inference time and accuracy in comparison to conventional methods. Moreover, it excels in terms of efficiency, generalizability, and interpretability when contrasted with other deep learning DOA estimation networks.Comment: 10 pages, 10 figure

M cells are involved in pathogenesis of human contact lens-associated giant papillary conjunctivitis

INTRODUCTION: The objective was to study the pathogenesis of contact lens-associated giant papillary conjunctivitis (CL-GPC). MATERIALS AND METHODS: Twenty-one biopsies of conjunctival giant papillae were obtained from soft contact lens wearers. The tissues were fixed in 4% paraformaldehyde and embedded in paraffin. Sections of 5 µm thickness were used for studies of histology and immunohistochemistry of pan-B and pan-T cell distributions. RESULTS: Conjunctival epitheliums on the top of conjunctiva-associated lymphoid tissue typically lacked goblet cells. Lymphocytes from underlying lymphoid follicle were pressed into intra-epithelial “pockets” formed through epithelial invagination. Under the follicle-associated epithelium, pan-B cells were mostly gathered in the central folliclar area and intraepithelial pockets, while CD3-positive T cells were predominantly distributed in parafolliclar region, but only a few in the intraepithelial pockets. CONCLUSIONS: Membranous epithelial cells (M cells) play a key role in the pathogenesis of CL-GPC for the binding and translocation of antigen and pathogen

SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images

There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease. However, existing methods either require a large pixel-wisely paired training dataset or have limited capability to map pathological regions. To address these issues, we proposed a structural constrained, pathology aware, transformer generative adversarial network, namely SCPAT-GAN, to generate virtual stained H&E histology from OCT images. The proposed SCPAT-GAN advances existing methods via a novel design to impose pathological guidance on structural layers using transformer-based network.Comment: 9 pages, 4 figure

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregard frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.Comment: 13 pages, 7 figures, submitted to Biomedical Optics Express special issu

Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation

Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation, offering the advantage of avoiding pixel-wise annotation. It is particularly appealing in medical image segmentation where annotation is laborious and expertise-demanding. However, the direct application of SAM often yields inferior results compared to conventional fully supervised segmentation networks. While using SAM generated pseudo label could also benefit the training of fully supervised segmentation, the performance is limited by the quality of pseudo labels. In this paper, we propose a novel label corruption to push the boundary of SAM-based segmentation. Our model utilizes a novel noise detection module to distinguish between noisy labels from clean labels. This enables us to correct the noisy labels using an uncertainty-based self-correction module, thereby enriching the clean training set. Finally, we retrain the network with updated labels to optimize its weights for future predictions. One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a subset of expert-level annotations. We demonstrate the effectiveness of our proposed model on both X-ray and lung CT datasets, indicating its ability to improve segmentation accuracy and outperform baseline methods in label correction

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure

The development of meibomian glands in mice

PurposeThe purpose of this study was to characterize the natural history of meibomian gland morphogenesis in the mouse.MethodsEmbryonic (E) and post natal (P) C57Bl/6 mouse pups were obtained at E18.5, P0, P1, P3, P5, P8, P15, and P60. Eyelids were fixed and processed for en bloc staining with Phalloidin/DAPI to identify gland morphogenesis, or frozen for immunohistochemistry staining with Oil red O (ORO) to identify lipid and antibodies specific against peroxisome proliferator-activated receptor gamma (PPARγ) to identify meibocyte differentiation. Samples were then evaluated using a Zeiss 510 Meta laser scanning confocal microscope or Nikon epi-fluorescent microscope. Tissues from adult mice (2 month-old) were also collected for western blotting.ResultsMeibomian gland morphogenesis was first detected at E18.5 with the formation of an epithelial placode within the fused eyelid margin. Invagination of the epithelium into the eyelid was detected at P0. From P1 to P3 there was continued extension of the epithelium into the eyelid. ORO and PPARγ staining was first detected at P3, localized to the central core of the epithelial cord thus forming the presumptive ductal lumen. Ductal branching was first detected at P5 associated with acinar differentiation identified by ORO and PPARγ staining. Adult meibomian glands were observed by P15. Western blotting of meibomian gland proteins identified a 50 kDa and a 72 kDa band that stained with antibodies specific to PPARγ.ConclusionsWe have demonstrated that meibomian gland development bears distinct similarities to hair development with the formation of an epithelial placode and expression of PPARγ co-incident with lipid synthesis and meibocyte differentiation

Large-conductance Ca2 +-activated K+ channel β1-subunit maintains the contractile phenotype of vascular smooth muscle cells

BackgroundVascular smooth muscle cells (VSMCs) phenotype switching is very important during the pathogenesis and progression of vascular diseases. However, it is not well understood how normal VSMCs maintain the differentiated state. The large-conductance Ca2+-activated K+ (BKCa) channels are widely expressed in VSMCs and regulate vascular tone. Nevertheless, there is limited understanding of the role of the BKCa channel in modulation of the VSMC phenotype.Methods and resultsWe assessed BKCa channel expression levels in normal and injured carotid arteries from rats of the balloon-injury model. A strong decrease of BKCa-β1 was seen in the injured carotid arteries, accompanied by a parallel decrease of the VSMC contractile markers. BKCa-β1 in primary rat aortic VSMCs was decreased with the increase of passage numbers and the stimulation of platelet-derived growth factor (PDGF)-BB. Conversely, transforming growth factor β upregulated BKCa-β1. Meanwhile, the BKCa-β1 level was positively associated with the levels of VSMC contractile proteins. Intravenous injection of PDGF-BB induced downregulation of BKCa-β1 expression in the carotid arteries. Knockdown of BKCa-β1 favored VSMC dedifferentiation, characterized by altered morphology, abnormal actin fiber organization, decreased contractile proteins expression and reduced contractile ability. Furthermore, the resultant VSMC dedifferentiated phenotype rendered increased proliferation, migration, enhanced inflammatory factors levels, and matrix metalloproteinases activity. Studies using primary cultured aortic VSMCs from human recapitulated key findings. Finally, protein level of BKCa-β1 was reduced in human atherosclerotic arteries.ConclusionBKCa-β1 is important in the maintenance of the contractile phenotype of VSMCs. As a novel endogenous defender that prevents pathological VSMC phenotype switching, BKCa-β1 may serve as a potential therapeutic target for treating vascular diseases including post-injury restenosis and atherosclerosis