Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in ...resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.
Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen ...or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 μm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.
Study of air pollution informs environmentally-conscious policies and urban planning by government and businesses, leading to more scientific decision-making. This paper comprehensively analyzes the ...spatiotemporal correlation of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) at the national level of China from 2015 to 2021. The temporal changes in environmental air pollutants are examined through statistical data analysis. Secondly, the annual and quarterly spatial distribution characteristics of air pollutants are analyzed by utilizing global and local spatial autocorrelation analysis and visualizing pollutant concentration data. To quantify the spatiotemporal correlation information obtained from the data, we proposed a novel framework based on the Geographically and Temporally Weighted Regression (GTWR) model to provide a local approach for detecting the spatiotemporal correlation between urban air pollutants on a large scale. The framework's experimental results showed that the significant correlation between urban air pollutants in time and space ranged from 0.45 to 0.9, which is better than traditional correlation algorithm, and the degree of local spatiotemporal correlations can be explained by spatiotemporal coefficients. We find noticeable differences in the correlations between different pollutants and geographical boundaries in the spatial dimension. Furthermore, in terms of spatiotemporal location, PM2.5, PM10, NO2, SO2, and CO exhibit positive correlations with each other, while O3 shows both positive and negative correlations with other pollutants. This study offers an important reference for air quality monitoring and prediction, contributing to the improvement of accuracy and timeliness of air quality warnings.
•Long-term Air pollutants trend at national level of China was explored.•A novel framework based on GTWR is proposed.•Considering spatial and temporal dependency can improve the model performance.•Local scale heterogeneity should be considered in future air pollution models.
In mobile edge computing (MEC), task offloading and resource allocation are two important issues that are inextricably linked. However, existing studies have either ignored the mobility of mobile ...users (MUs) during task offloading or the allocation of profits between two parties during the allocation of limited resources (i.e., the resource competition). In this paper, we jointly optimized these two problems. First, to reduce the task offloading delay and the service interruption due to movement, we develop a mobility-aware model, based on which we propose the MWBS algorithm to select the appropriate offloading base station (BS) for MUs. Second, considering the resource competition and the delay constraint of the task, we develop a double auction model and then propose the DARA algorithm, which efficiently allocates the BS resources and maximizes the total system revenue (i.e., social welfare) through a multi-session auction. Finally, we combine MWBS and DARA to propose the BS resource allocation algorithm called MD-BSRA in mobile scenarios. Simulation results show that MD-BSRA can effectively improve task offload success rate, total system revenue and resource utilization while reducing offload delay and service interruption.
Histopathology is the gold standard for cancer diagnosis, but histopathology slide preparation is expensive, time- and labor-intensive. Slide-free pathology with fluo- rescence microscopy could offer ...a faster and less costly alternative. However, rapidly imaging intact tissue with surface irregularities (∼ 200μm) is fundamentally con- strained by the intrinsic trade-off between resolution and depth-of-field (DOF). In this study, we present a novel computational microscope that can image intact spec- imens with cellular resolution without re-focusing. The system is designed to pro- vide real-time deep learning based histopathology of intact specimen using extended depth-of-field (DeepDOF).the DeepDOF microscope consists of a conventional microscope with the addition of a wavefront-encoding phase mask and a neural network that jointly extends the DOF while maintaining subcellular resolution. Leveraging advances in deep learning, we simultaneously designed and optimized the two key components in the DeepDOF network end-to-end. First, the optical layer simulates the phase mask that creates a depth-dependent and invertible point spread function (PSF). These PSF sections can encode surface texture/intensity information regardless of the surface topology. Sec- ond, an artificial intelligence-based digital layer is used to deconvolve and extract high resolution image information from the captured data. In this study, we trained the DeepDOF network and optimized the microscope design with a large image dataset consisting of varied imaging features from human histology to natural scenes. The optimized phase mask was then fabricated using reactive ion etching and inserted into the aperture plane of a 4x, 0.13 NA epi-fluorescence microscope, which was further integrated with an automated x-y sample stage for tissue mapping. We calibrated the depth dependent PSFs of the DeepDOF microscope using 1 μm fluorescent beads.By imaging resolution target, we show that the DeepDOF microscope can con- sistently resolve subcellular features within a 200 μm depth-of-field, thus allowing the visualization of nuclear morphology on highly irregular tissue surfaces without serial focusing. We validated DeepDOF microscope’s performance by imaging freshly resected and proflavine-stained porcine esophageal tissue and human oral tissue. Fur- thermore, we show that DeepDOF images reveal a variety of important diagnostic features confirmed by standard histopathology. In the long term, the DeepDOF mi- croscope can substantially contribute to histopathological assessment of intact biop- sies and surgical specimens, especially for intraoperative evaluation and in resource- constrained settings.
Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy. A promising strategy to overcome this challenge is wavefront ...modulation, which induces measurement diversity during image acquisition. Despite its importance, designing optimal wavefront modulations to image through scattering remains under-explored. This paper introduces a novel learning-based framework to address the gap. Our approach jointly optimizes wavefront modulations and a computationally lightweight feedforward "proxy" reconstruction network. This network is trained to recover scenes obscured by scattering, using measurements that are modified by these modulations. The learned modulations produced by our framework generalize effectively to unseen scattering scenarios and exhibit remarkable versatility. During deployment, the learned modulations can be decoupled from the proxy network to augment other more computationally expensive restoration algorithms. Through extensive experiments, we demonstrate our approach significantly advances the state of the art in imaging through scattering media. Our project webpage is at https://wavemo-2024.github.io/.
Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand. Intricate vessel-based measurements, such as intimal wall thickness and ...vessel wall compliance, require sub-millimeter vessel tracking between B-scans. Our fast GPU-based approach combines the advantages of local phase analysis, a distance-regularized level set, and an Extended Kalman Filter (EKF), to rapidly segment and track the deforming vessel contour. We validated on 35 UHFUS sequences of vessels in the hand, and we show the transferability of the approach to 5 more diverse datasets acquired by a traditional High Frequency Ultrasound (HFUS) machine. To the best of our knowledge, this is the first algorithm capable of rapidly segmenting and tracking deformable vessel contours in 2D UHFUS images. It is also the fastest and most accurate system for 2D HFUS images.
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