It verifies that the space around the detected speckle is empty. To do this efficiently, an integral image of the binarized depth map is computed, which allows for rapid, constant-time queries of the pixel sum within any rectangular region. We check the pixel sum in eight rectangular regions surrounding the speckle's bounding box. If the ratio of filled pixels to total pixels within these regions is below a low threshold (e.g., 0.07), the speckle is considered isolated within a glass plane. T emporal Consistency: A final filter operates on a tracking-by-detection principle to ensure identified features are persistent and not transient sensor noise. A speckle is confirmed and passed to the mapping algorithm only after its required count (e.g., 1-3 detections) is exceeded across multiple consecutive frames. To prevent the accumulation of false positives and old detections, a max age parameter is used to expire and remove tracks that have not been seen for a specified duration. D. Transparent Plane Reprojection The final stage of our methodology involves segmenting empty regions in the depth map and reprojecting the confirmed transparent planes. The algorithm first identifies the empty regions in the depth image and applies a non-maximum suppression (NMS) algorithm to merge redundant empty regions, ensuring a single, accurate representation of each transparent plane.

Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) imaging, where speckle noise significantly degrades image quality. Although recent advancements in deep neural networks have led to substantial improvements in denoising for natural images, these methods cannot be directly applied to US speckle noise, as it is not purely random. Instead, US speckle arises from complex wave interference within the body microstructure, making it tissue-dependent. This dependency means that obtaining two independent noisy observations of the same scene, as required by pioneering Noise2Noise, is not feasible. Additionally, blind-spot networks also cannot handle US speckle noise due to its high spatial dependency. To address this challenge, we introduce Speckle2Self, a novel self-supervised algorithm for speckle reduction using only single noisy observations. The key insight is that applying a multi-scale perturbation (MSP) operation introduces tissue-dependent variations in the speckle pattern across di fferent scales, while preserving the shared anatomical structure. This enables e ff ective speckle suppression by modeling the clean image as a low-rank signal and isolating the sparse noise component. To demonstrate its e ff ectiveness, Speckle2Self is comprehensively compared with conventional filter-based denoising algorithms and SOT A learning-based methods, using both realistic simulated US images and human carotid US images. Additionally, data from multiple US machines are employed to evaluate model generalization and adaptability to images from unseen domains. Introduction Medical ultrasound (US) is one of the most important imaging modalities in modern clinical practices due to its a ff ord-ability, non-invasiveness and real-time capabilities Jiang et al. (2023a); Bi et al. (2023b). US imaging visualises internal anatomical structures by emitting high-frequency acoustic waves (typically 2 15 MHz) into the body and detecting echoes scattered from tissue interfaces Szabo (2013). Compared to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), US images generally su ff er from lower image quality Kang et al. (2024); Stevens et al. (2024); Calis et al. (2025); Mwikirize et al. (2018), primarily due to speckle noise--one of the most prominent artefacts in B-mode imaging. This speckle noise arises from the coherent summation of echoes scattered by small-scale tissue structures (e.g., cells) and manifests as grainy patterns that degrade image clarity and contrast Krissian et al. (2005). This work involved human subjects in its research. Approval of all ethical and experimental procedures and protocols was granted by Institutional Review Board, No. 2022-87-S-KK, Declaration of Helsinki.

The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach, leveraging problem symmetries and a joint spectral channel representation grounded in physical principles. Our model integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation. The proposed algorithm is evaluated against the state of the art using datasets from the SPHERE instrument operating at the Very Large Telescope (VLT). It significantly improves the precision-recall trade-off, notably on challenging datasets that are otherwise unusable by astronomers. The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.

Research ultrasound scanners such as the Verasonics Vantage often lack the advanced image processing algorithms used by clinical systems. Image quality is even lower in plane wave imaging - often used for shear wave elasticity imaging (SWEI) - which sacrifices spatial resolution for temporal resolution. As a result, delay-and-summed images acquired from SWEI have limited interpretability. In this project, a two-stage machine learning model was trained to enhance single plane wave images of muscle acquired with a Verasonics Vantage system. The first stage of the model consists of a U-Net trained to emulate plane wave compounding, histogram matching, and unsharp masking using paired images. The second stage consists of a CycleGAN trained to emulate clinical muscle B-modes using unpaired images. This two-stage model was implemented on the Verasonics Vantage research ultrasound scanner, and its ability to provide high-speed image formation at a frame rate of 28.5 +/- 0.6 FPS from a single plane wave transmit was demonstrated. A reader study with two physicians demonstrated that these processed images had significantly greater structural fidelity and less speckle than the original plane wave images.

In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. Through initial experimentation, we identified six pertinent first-order logical rules, which were then used to train a Logic-Understanding (LU) model capable of generating logical triples for a given context, question, and answer. These logic triples are then integrated into the training of MedLogic-AQA, enabling effective and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging. Evaluation with respect to both automated and human-based demonstrates the robustness of MedLogic-AQA against strong baselines. Through empirical assessments and case studies, we validate the efficacy of MedLogic-AQA in elevating the quality and comprehensiveness of answers in terms of reasoning as well as informativeness

Abstract Background During percutaneous puncture robotic surgical navigation, the needle insertion point is positioned on the patient's chest and abdomen body surface. By locating any point on the soft skin tissue, it is difficult to apply the traditional reflective ball tracking method. The patient's chest and abdomen body surface has fluctuations in breathing and appears irregular. The chest and abdomen are regular and smooth, lacking obvious features, and it is challenging to locate the needle insertion point on the body surface. Methods This paper designs and experiments a method that is different from previous reflective ball optical markers or magnetic positioning surgical navigation and tracking methods. It is based on a speckle structured light camera to identify the patient's body surface and fit it into a hollow ring with a diameter of 24mm. Results Experimental results show that this method of the system can be small, flexible, and high-precision positioning of any body surface point at multiple angles, achieving a positioning accuracy of 0.033-0.563mm and an image of 7-30 frames/s. Conclusions The positioning recognition ring material used in this method can be well imaged under CT, so the optical positioning of the body surface and the in vivo imaging positioning under CT can be combined to form a unified patient's internal and external positioning world coordinates to achieve internal and external registration. Positioning integration. The system senses motion with six degrees of freedom, up and down, front and back, left and right, and all rotations, with sub-millimeter accuracy, and has broad application prospects in future puncture surgeries.

In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings but without any prior knowledge of their characteristics. In the literature, most model-based algorithms face three main issues. First, the speckle noise corrupts the image and potentially leads to numerous false detections. Second, statistical approaches may exhibit deficiencies in modeling spatial correlation in SAR images. Finally, neural networks based on supervised learning approaches are not recommended due to the lack of annotated SAR data, notably for the class of abnormal patterns. Our proposed method aims to address these issues through a self-supervised algorithm. The speckle is first removed through the deep learning SAR2SAR algorithm. Then, an adversarial autoencoder is trained to reconstruct an anomaly-free SAR image. Finally, a change detection processing step is applied between the input and the output to detect anomalies. Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.

Background: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. Methods: We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. Results: By borrowing strengths from multiple classification models through the proposed method, we improve the CHD classification accuracy from around 70% to 87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. Conclusions: Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.

Abstract--In recent years, Machine Learning (ML) algorithms have become widespread in all fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed a rapid development of new procedures to solve problems affecting these sectors. In this context, the authors of this work aim to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), by proving the effectiveness of the proposed architecture. Moreover, the generated open-source code and dataset have been made available for further developments and investigation by interested researchers.

In this study we investigate the potential for using Synthetic Aperture Radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using in situ measurements collected in 2017 we calculated the proportion of both live and defoliated tree crown for 165 $10 m \times 10 m$ ground plots along six transects. Quad-polarimetric SAR data from RADARSAT-2 was collected from the same area, and the complex multilook polarimetric covariance matrix was calculated using a novel extension of guided nonlocal means speckle filtering. The nonlocal approach allows us to preserve the high spatial resolution of single-look complex data, which is essential for accurate mapping of the sparsely scattered trees in the study area. Using a standard random forest classification algorithm, our filtering results in a $73.8 \%$ classification accuracy, higher than traditional speckle filtering methods, and on par with the classification accuracy based on optical data.