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• New water-soluble Aβ probes based on AIE mechanism were developed. • The probes showed more remarkable affinity to Aβ aggregates than commercial ThT. • The probes possessed rapid staining and washing-free imaging to Aβ plaques. • An efficient approach for mapping of Aβ plaques in whole brain was developed. Rapid verification and three-dimensional (3D) mapping of β-amyloid (Aβ) plaques in mouse whole brain is of great significance in early diagnosis of Alzheimer's disease (AD) due to the intricate relationship of Aβ plaques with their surrounding microenvironment. Previously reported fluorescent labelling to locate Aβ plaques usually require lengthy permeation/staining/washing procedures to eliminate non-specific binding and improve fluorescence signal-to-noise ratio, which is inaccessible to automatic brain wide 3D imaging. Here we report a kind of water-soluble fluorescent probes with aggregation-induced emission (AIE) activities, named AIEgens, which can rapidly permeate and stain Aβ plaques of transgenic (Tg) mouse brain sections into 8 μm of depth within 1 min. The excellent water-solubility, specificity and sensitivity of AIEgens for Aβ plaques enable rapid staining and washing-free imaging of brain sections. The automatic 3D mapping of Aβ plaques in Tg mouse's whole brain is implemented through the cycling process of in situ real-time mechanical sectioning, staining and imaging with AIEgens in fluorescent micro-optical sectioning tomography (fMOST) system. The AIE-based fMOST, which integrates the in-situ staining/imaging/sectioning steps of Tg mouse's whole brain, provides an automatic 3D mapping solution to access the size, morphology and 3D distribution of Aβ plaques in mouse whole brain, which would help to explore the correlation between Aβ structure and neurodegenerative activity. [ABSTRACT FROM AUTHOR]

• New water-soluble Aβ probes based on AIE mechanism were developed. • The probes showed more remarkable affinity to Aβ aggregates than commercial ThT. • The probes possessed rapid staining and washing-free imaging to Aβ plaques. • An efficient approach for mapping of Aβ plaques in whole brain was developed. Rapid verification and three-dimensional (3D) mapping of β-amyloid (Aβ) plaques in mouse whole brain is of great significance in early diagnosis of Alzheimer's disease (AD) due to the intricate relationship of Aβ plaques with their surrounding microenvironment. Previously reported fluorescent labelling to locate Aβ plaques usually require lengthy permeation/staining/washing procedures to eliminate non-specific binding and improve fluorescence signal-to-noise ratio, which is inaccessible to automatic brain wide 3D imaging. Here we report a kind of water-soluble fluorescent probes with aggregation-induced emission (AIE) activities, named AIEgens, which can rapidly permeate and stain Aβ plaques of transgenic (Tg) mouse brain sections into 8 μm of depth within 1 min. The excellent water-solubility, specificity and sensitivity of AIEgens for Aβ plaques enable rapid staining and washing-free imaging of brain sections. The automatic 3D mapping of Aβ plaques in Tg mouse's whole brain is implemented through the cycling process of in situ real-time mechanical sectioning, staining and imaging with AIEgens in fluorescent micro-optical sectioning tomography (fMOST) system. The AIE-based fMOST, which integrates the in-situ staining/imaging/sectioning steps of Tg mouse's whole brain, provides an automatic 3D mapping solution to access the size, morphology and 3D distribution of Aβ plaques in mouse whole brain, which would help to explore the correlation between Aβ structure and neurodegenerative activity. [ABSTRACT FROM AUTHOR]

[Display omitted] To decipher the organizational logic of complex brain circuits, it is important to chart long-distance pathways while preserving micron-level accuracy of local network. However, mapping the neuronal projections with individual-axon resolution in the large and complex primate brain is still challenging. Herein, we describe a highly efficient pipeline for three-dimensional mapping of the entire macaque brain with subcellular resolution. The pipeline includes a novel poly-N-acryloyl glycinamide (PNAGA)-based embedding method for long-term structure and fluorescence preservation, high-resolution and rapid whole-brain optical imaging, and image post-processing. The cytoarchitectonic information of the entire macaque brain was acquired with a voxel size of 0.32 μm × 0.32 μm × 10 μm, showing its anatomical structure with cell distribution, density, and shape. Furthermore, thanks to viral labeling, individual long-distance projection axons from the frontal cortex were for the first time reconstructed across the entire brain hemisphere with a voxel size of 0.65 μm × 0.65 μm × 3 μm. Our results show that individual cortical axons originating from the prefrontal cortex simultaneously target multiple brain regions, including the visual cortex, striatum, thalamus, and midbrain. This pipeline provides an efficient method for cellular and circuitry investigation of the whole macaque brain with individual-axon resolution, and can shed light on brain function and disorders. [ABSTRACT FROM AUTHOR]

Over recent years, blind quality estimators for three-dimensional (3D) images have received increasing attention. In this paper, we describe a blind quality estimator for 3D images based on binocular combination and an extreme learning machine (ELM) for more accurate alignment with subjective human experience. First, two binocular combinations of stimuli are generated using different combination strategies. Various binocular quality-aware features of these combinations are then extracted by local binary pattern operators, which give an effective description of the degradation pattern. Finally, these features are mapped to the subjective quality score of the distorted 3D image using an ELM. Experimental results using three benchmark databases confirm that the proposed metric is effective and achieves competitive prediction performance when compared with most current full reference and blind metrics. [ABSTRACT FROM AUTHOR]

• To relieve image distortion caused by embedding watermark, BAM is designed by exploring the correlations between the center view and depth map to compute attention of 3D images, which guide watermark to be embedded into inconspicuous regions for the high-quality image. • In order to bridge the cross-view gap between the center view and the depth map from different domains, CMFFM is proposed to fuse features from these two domains for computation of BAM. • Since the depth map includes a lot of spatial redundancy information which degrades the quality of feature fusion between two views for BAM, DREM is deployed by using inter-channel and intra-channel weights to prune the redundant features of the depth map. This paper presents a bilateral attention based generative adversarial network (BAGAN) for depth-image-based rendering (DIBR) 3D image watermarking to protect the image copyright. Convolutional block operations are employed to extract main image features for robust watermarking, but embedding watermark into some features will degrade image quality much. To relieve this kind of image distortion, the bilateral attention module (BAM) is utilized by mining correlations of the center view and the depth map to compute attention of the 3D image for guiding watermark to distribute over different image regions. Since a modality gap exists between the center view and the depth map, a cross-modal feature fusion module (CMFFM) is designed for BAM to bridge the cross-view gap. Because the depth map has lots of flat background information including many redundant features, to prune them, the depth redundancy elimination module (DREM) is used for cross-view feature fusion. In the decoder, two extractors with the same structure are built to recover watermark from the center view and the synthesized view, respectively. In addition, the discriminator is supposed to build a competitive relationship with the encoder to increase the image quality. The noise sub-network is used to train different image attacks for robustness. Extensive experimental results have demonstrated that the proposed BAGAN can obtain higher watermarking invisibility and robustness compared with existing DIBR 3D watermarking methods. Ablation experiments have also proven the effectiveness of DREM, CMFFM and BAM on BAGAN. [ABSTRACT FROM AUTHOR]

The perceived quality assessment of three-dimensional (3D) images has emerged as a challenging research topic in the field of 3D imaging in recent years. Especially, blind quality assessment of 3D images encounters challenges due to prior information about the original 3D images is not available. In this paper, we propose a blind 3D image quality assessment (IQA) metric that utilizes the self-similarity of binocular features. The primary contribution of this study is that the proposed metric considers the binocular visual property and the local visual structural property for blind 3D-IQA. We calculate the self-similarity of binocular rivalry response as well as binocular orientation selectivity in the distorted 3D image. We then extract the inter- and intra-pixel binocular quality-predictive features from these self-similarity measures. Following feature extraction, we use machine learning based on the support vector regression (SVR) procedure to drive the overall quality score. Our results on publicly accessible 3D databases confirmed that the proposed metric is highly efficient and robust. [ABSTRACT FROM AUTHOR]

In the field of practical three-dimensional (3D) applications, blind measurement of the perceptual quality of distorted 3D images remains a challenging research topic. In this paper, we propose a completely blind 3D image quality measurement (IQM) metric that utilizes a binocular vision mechanism to better align with human perception. As its primary focus, this study is inspired by the visual processing in the primary visual cortex (V1) and the higher visual areas (V2) of binocular vision to facilitate blind 3D-IQM. Furthermore, the proposed metric does not require distorted samples or human subjective opinion scores for training. More specifically, the binocular quality-predictive features of areas V1 and V2 are first extracted from a corpus of pristine natural 3D images. Subsequently, a pristine multivariate Gaussian (MVG) model is trained from the extracted features. Finally, with the trained MVG model, the quality of distorted 3D images is measured using a Mahalanobis distance. Experimental results using two public benchmark 3D databases show that in comparison with current state-of-the-art IQM metrics, the proposed metric achieves excellent prediction performance. [ABSTRACT FROM AUTHOR]

In the past decade, various fragile digital watermarking techniques have been proposed for monoscopic image authentication and temper detection. In this paper, a novel binocular visual characteristics based pixel-wise fragile watermarking scheme for stereoscopic image authentication and tamper detection is proposed. The scheme consists of two processes: embedding of the stereoscopic image authentication message and tamper detection. In the watermark embedding process, the binocular just noticeable difference (BJND) model is used for guiding watermark embedding, which is convenient for achieving a balanced relationship between watermarking capacity and visual transparency. In the tamper detection process, a probability theory and an algorithm of binocular disparity are employed to improve previously obtained detection results and to enhance authentication accuracy. Moreover, to improve the security of the proposed scheme, MD5 hash function and chaotic map are used. Experimental results reveal that the proposed scheme is not only secure, but also achieves superior tamper detection for different attacks. [ABSTRACT FROM AUTHOR]