Adolescent idiopathic scoliosis (AIS) is a common spinal malalignment affecting approximately 2.2% of boys and 4.8% of girls worldwide. The Cobb angle serves as the gold standard for AIS severity assessment, yet traditional manual measurements suffer from significant observer variability, compromising diagnostic accuracy. Despite prior automation attempts, existing methods use simplified spinal models and predetermined curve patterns that fail to address clinical complexity. We present a novel deep learning framework for AIS assessment that simultaneously predicts both superior and inferior endplate angles with corresponding midpoint coordinates for each vertebra, preserving the anatomical reality of vertebral wedging in progressive AIS. Our approach combines an HRNet backbone with Swin-Transformer modules and biomechanically informed constraints for enhanced feature extraction. We employ Singular Value Decomposition (SVD) to analyze angle predictions directly from vertebral morphology, enabling flexible detection of diverse scoliosis patterns without predefined curve assumptions. Using 630 full-spine anteroposterior radiographs from patients aged 10-18 years with rigorous dual-rater annotation, our method achieved 83.45% diagnostic accuracy and 2.55$^{\circ }$ mean absolute error. The framework demonstrates exceptional generalization capability on out-of-distribution cases. Additionally, we introduce the Vertebral Wedging Index (VWI), a novel metric quantifying vertebral deformation. Longitudinal analysis revealed VWI's significant prognostic correlation with curve progression while traditional Cobb angles showed no correlation, providing robust support for early AIS detection, personalized treatment planning, and progression monitoring.

Adolescent Idiopathic Scoliosis (AIS) is a complex three-dimensional spinal deformity, and accurate morphological assessment requires evaluating both coronal and sagittal alignment. While previous research has made significant progress in developing radiation-free methods for coronal plane assessment, reliable and accurate evaluation of sagittal alignment without ionizing radiation remains largely underexplored. To address this gap, we propose LatXGen, a novel generative framework that synthesizes realistic lateral spinal radiographs from posterior Red-Green-Blue and Depth (RGBD) images of unclothed backs. This enables accurate, radiation-free estimation of sagittal spinal alignment. LatXGen tackles two core challenges: (1) inferring sagittal spinal morphology changes from a lateral perspective based on posterior surface geometry, and (2) performing cross-modality translation from RGBD input to the radiographic domain. The framework adopts a dual-stage architecture that progressively estimates lateral spinal structure and synthesizes corresponding radiographs. To enhance anatomical consistency, we introduce an attention-based Fast Fourier Convolution (FFC) module for integrating anatomical features from RGBD images and 3D landmarks, and a Spatial Deformation Network (SDN) to model morphological variations in the lateral view. Additionally, we construct the first large-scale paired dataset for this task, comprising 3,264 RGBD and lateral radiograph pairs. Experimental results demonstrate that LatXGen produces anatomically accurate radiographs and outperforms existing GAN-based methods in both visual fidelity and quantitative metrics. This study offers a promising, radiation-free solution for sagittal spine assessment and advances comprehensive AIS evaluation.

Depth estimation from light field (LF) images is a fundamental step for numerous applications. Recently, learning-based methods have achieved higher accuracy and efficiency than the traditional methods. However, it is costly to obtain sufficient depth labels for supervised training. In this paper, we propose an unsupervised framework to estimate depth from LF images. First, we design a disparity estimation network (DispNet) with a coarse-to-fine structure to predict disparity maps from different view combinations. It explicitly performs multi-view feature matching to learn the correspondences effectively. As occlusions may cause the violation of photo-consistency, we introduce an occlusion prediction network (OccNet) to predict the occlusion maps, which are used as the element-wise weights of photometric loss to solve the occlusion issue and assist the disparity learning. With the disparity maps estimated by multiple input combinations, we then propose a disparity fusion strategy based on the estimated errors with effective occlusion handling to obtain the final disparity map with higher accuracy. Experimental results demonstrate that our method achieves superior performance on both the dense and sparse LF images, and also shows better robustness and generalization on the real-world LF images compared to the other methods.

Light field (LF) images containing information for multiple views have numerous applications, which can be severely affected by low-light imaging. Recent learning-based methods for low-light enhancement have some disadvantages, such as a lack of noise suppression, complex training process and poor performance in extremely low-light conditions. To tackle these deficiencies while fully utilizing the multi-view information, we propose an efficient Low-light Restoration Transformer (LRT) for LF images, with multiple heads to perform intermediate tasks within a single network, including denoising, luminance adjustment, refinement and detail enhancement, achieving progressive restoration from small scale to full scale. Moreover, we design an angular transformer block with an efficient view-token scheme to model the global angular dependencies, and a multi-scale spatial transformer block to encode the multi-scale local and global information within each view. To address the issue of insufficient training data, we formulate a synthesis pipeline by simulating the major noise sources with the estimated noise parameters of LF camera. Experimental results demonstrate that our method achieves the state-of-the-art performance on low-light LF restoration with high efficiency.

Abstract The optimization problems with a sparsity constraint is a class of important global optimization problems. A typical type of thresholding algorithms for solving such a problem adopts the traditional full steepest descent direction or Newton-like direction as a search direction to generate an iterate on which a certain thresholding is performed. Traditional hard thresholding discards a large part of a vector, and thus some important information contained in a dense vector has been lost in such a thresholding process. Recent study (Zhao in SIAM J Optim 30(1): 31–55, 2020) shows that the hard thresholding should be applied to a compressible vector instead of a dense vector to avoid a big loss of information. On the other hand, the optimal k -thresholding as a novel thresholding technique may overcome the intrinsic drawback of hard thresholding, and performs thresholding and objective function minimization simultaneously. This motivates us to propose the so-called partial gradient optimal thresholding (PGOT) method and its relaxed versions in this paper. The PGOT is an integration of the partial gradient and the optimal k -thresholding technique. The solution error bound and convergence for the proposed algorithms have been established in this paper under suitable conditions. Application of our results to the sparse optimization problems arising from signal recovery is also discussed. Experiment results from synthetic data indicate that the proposed algorithm is efficient and comparable to several existing algorithms.

We consider the problem of high-dimensional light field reconstruction and develop a learning-based framework for spatial and angular super-resolution. Many current approaches either require disparity clues or restore the spatial and angular details separately. Such methods have difficulties with non-Lambertian surfaces or occlusions. In contrast, we formulate light field super-resolution (LFSR) as tensor restoration and develop a learning framework based on a two-stage restoration with 4-dimensional (4D) convolution. This allows our model to learn the features capturing the geometry information encoded in multiple adjacent views. Such geometric features vary near the occlusion regions and indicate the foreground object border. To train a feasible network, we propose a novel normalization operation based on a group of views in the feature maps, design a stage-wise loss function, and develop the multi-range training strategy to further improve the performance. Evaluations are conducted on a number of light field datasets including real-world scenes, synthetic data, and microscope light fields. The proposed method achieves superior performance and less execution time comparing with other state-of-the-art schemes.

This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images. A key challenge for this novel view synthesis arises from the reconstruction process, when the views from different input images may not be consistent due to obstruction in the light path. We overcome this by jointly modeling the epipolar property and occlusion in designing a convolutional neural network. We start by defining and computing the aperture disparity map, which approximates the parallax and measures the pixel-wise shift between two views. While this relates to free-space rendering and can fail near the object boundaries, we further develop a warping confidence map to address pixel occlusion in these challenging regions. The proposed method is evaluated on diverse real-world and synthetic light field scenes, and it shows better performance over several state-of-the-art techniques.

Plenoptic cameras usually sacrifice the spatial resolution of their SAIs to acquire geometry information from different viewpoints. Several methods have been proposed to mitigate such spatio-angular trade-off, but seldom make use of the structural properties of the light field (LF) data efficiently. In this paper, we propose a novel high-order residual network to learn the geometric features hierarchically from the LF for reconstruction. An important component in the proposed network is the high-order residual block (HRB), which learns the local geometric features by considering the information from all input views. After fully obtaining the local features learned from each HRB, our model extracts the representative geometric features for spatio-angular upsampling through the global residual learning. Additionally, a refinement network is followed to further enhance the spatial details by minimizing a perceptual loss. Compared with previous work, our model is tailored to the rich structure inherent in the LF, and therefore can reduce the artifacts near non-Lambertian and occlusion regions. Experimental results show that our approach enables high-quality reconstruction even in challenging regions and outperforms state-of-the-art single image or LF reconstruction methods with both quantitative measurements and visual evaluation.

Light field (LF) cameras often have significant limitations in spatial and angular resolutions due to their design. Many techniques that attempt to reconstruct LF images at a higher resolution only consider either spatial or angular resolution, but not both. We propose a generative network using high-dimensional convolution to improve both aspects. Our experimental results on both synthetic and real-world data demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of both peak signal-to-noise ratio (PSNR) and visual quality. The proposed method can also generate more realistic spatial details with better fidelity.

Recent advances in ultra-high-throughput microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotypes alone. In contrast to current single-cell analysis techniques that rely solely on slow and costly genetic/epigenetic analysis, these image-based analyses allow morphological profiling and screening of thousands or even millions of single cells at a fraction of the cost, and have been proven to demonstrate the statistical significance required for understanding the role of cell heterogeneity in diverse biological applications, ranging from cancer screening to drug candidate identification/validation processes. This paper examines the efficacies and opportunities presented by machine learning algorithms in processing large scale datasets with millions of label-free cell images. An automatic single-cell classification framework using convolutional neural network (CNN) has been developed. A comparative analysis of its efficiency in classifying large datasets against conventional k-nearest neighbors (kNN) and support vector machine (SVM) based methods are also presented. Experiments have shown that our proposed framework can efficiently identify multiple types cells with over 99% accuracy based on the phenotypic label-free bright-field images; and CNN-based models perform well and relatively stable against data volume compared with kNN and SVM.

In this paper, we present a sparse hierarchical non-parametric Bayesian (SHNB) model, which is used to represent the data captured by the light field cameras. Specifically, a light field can be represented as a set of sub-aperture views. In order to capture the visual variations of these viewpoints, we propose the so-called “depth flow” features. Then based on the depth flow features, we model these views statistically with a sparse representation in a fully unsupervised manner. While local dictionaries are learned based on each sub-aperture view, all the views with different perspectives share one global dictionary. To show the effectiveness of the proposed model, we apply our model to denoise the light field data. In the experiments, we demonstrate that our method outperforms several state-of-the-art light field denoising approaches.

This paper presents a data-driven approach to estimate the object depths from light field data using Convolutional Neural Networks (CNN). By exploring the relationship between the epipolar-plane images (EPI) and the corresponding depth map, we propose an enhanced EPI feature that encodes the depth information of each physical point in the light field and obtains the disparity map of the whole scene in a supervised manner. This work covers two major contributions, namely the extraction of the enhanced EPI features and the light field depth estimation with CNN. The proposed features augment the depth information of the corresponding points in the light field, and then our CNN architecture differentiates them into different depth layers. Forward propagation step of the CNN model allows rapid recognition of the disparity map of the test light field data. In the experiments, we apply our method on the HCI (Heidelberg Col-laboratory for Image Processing) benchmark dataset and demonstrate that it is significantly faster than the state-of-the-art light field depth estimation approaches while achieving satisfactory performance.

BACKGROUND: Identification and analysis of recurrent combinatorial patterns of multiple chromatin modifications provide invaluable information for understanding epigenetic regulations. Furthermore, as more data becomes available, it is computationally expensive and unnecessary to study combinatorial patterns of all modifications. METHODS: A novel framework is proposed to investigate recurrent combinatorial patterns of a subset of quantitatively selected chromatin modifications. The framework is based on heirarchical clustering and selects subsets of chromatin modifications that form distinct recurrent patterns at regulatory regions. The identified recurrent combinatorial patterns can be further utilized to discover novel regulatory regions. Data is in the form of genome wide maps of histone acetylations, methylations, and histone variant of human skeletal muscular and B-lymphocyte cells both derived from the ENCODE project. RESULTS: A case study conducted at promoter regions is presented: four out of twelve chromatin modifications were selected, eight different promoter states were identified and the identified patterns of active promoters were further utilized to discover novel promoter regions. Several previously un-annotated promoters were discovered, further investigations confirm their promoter functions. CONCLUSIONS: This framework is approproiately general and could lead to better understanding of epigenetic regulations by discovering previously unknown regulatory regions.

Histone modification is an important epigenetic event which plays essential roles in cell differentiation and tissue development. Recent studies show that a unique dimethylation of lysine 4 residue on histone 3 (H3K4me2) distribution pattern around transcription starting sites (TSS) of genes marks tissue specific genes in human CD4 þ T cells and mouse nervous tissue cells. However, existence of this pattern has not been widely tested and its implication remains unclear. In this paper, we study the H3K4me2 distribution patterns across six different cell lines from five major tissue types (including muscular tissue, nervous tissue, non-blood connective tissue, blood, and epithelial tissue) as well as embryonic stem cells. We define a metric ‘tail length’ to quantitatively describe H3K4me2 distribution patterns around the TSS. While confirming the previous observations, we also identified a group of 217 genes with ubiquitous long-tail H3K4me2 patterns in all the tested tissues and the embryonic stem cells (ESC). Further analyses confirmed that these genes are critical for development, and highly interactive with other tissue specific genes as evinced by protein-protein interaction networks, suggesting their critical regulatory functions. Our results suggest that rich information on gene functions and epigenetic events can be revealed using pattern recognition methods.