Spectral X-ray CT for fast NDT using discrete tomography

We present progress in fast, high-resolution imaging, material classification, and fault detection using hyperspectral X-ray measurements. Classical X-ray CT approaches rely on data from many projection angles, resulting in long acquisition and reconstruction times. Additionally, conventional CT cannot distinguish between materials with similar densities. However, in additive manufacturing, the majority of materials used are known a priori. This knowledge allows to vastly reduce the data collected and increase the accuracy of fault detection. In this context, we propose an imaging method for non-destructive testing of materials based on the combination of spectral X-ray CT and discrete tomography. We explore the use of spectral X-ray attenuation models and measurements to recover the characteristic functions of materials in heterogeneous media with piece-wise uniform composition. We show by means of numerical simulation that using spectral measurements from a small number of angles, our approach can alleviate the typical deterioration of spatial resolution and the appearance of streaking artifacts.Mechanical Engineerin

Joseph the MoUSE : Mouse Ultrasonic Sound Explorer

Joseph the MoUSE — Mouse Ultrasonic Sound Explorer (MoUSE) software aims to address the issue of manual analysis of recordings from experiments on rodents by introducing automatic techniques for ultrasonic vocalization (USV) detection. It combines deep learning (DL) methods with classical pattern recognition and computer graphics algorithms. During development, we used a dataset that consisted of recordings from real-world experiments in the open field. Recordings like these pose obstacles to automatic USV detection, one of which is the noise produced by mice in the experimental area or in nearby cages. Therefore, additionally, we conducted research and implemented de-noising methods along with detection algorithms. The project includes Python packages with algorithms for sound noise removal and USV detection, and provides a user-friendly graphical interface

End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current deep-learning-based approaches fail to delineate all building instances and do so with adequate accuracy. As a solution, we present Trainable Deep Active Contours (TDACs), an automatic image segmentation framework that intimately unites Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs). The Eulerian energy functional of the ACM component includes per-pixel parameter maps that are predicted by the backbone CNN, which also initializes the ACM. Importantly, both the ACM and CNN components are fully implemented in TensorFlow and the entire TDAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image. We validate the model on two publicly available aerial image datasets for building segmentation, and our results demonstrate that TDAC establishes a new state-of-the-art performance.Comment: Accepted to European Conference on Computer Vision (ECCV) 202

Monitoring Summertime Erosion Patterns Over an Arctic Permafrost Coast with Recent Sub-meter Resolution Microsatellite SAR Data

Arctic coasts experience some of the highest rates of erosion in the world, particularly due to permafrost degradation resulting from the recent exacerbation of climate change. Therefore, not only have coastal defense and energy facilities been threatened, but maintenance costs for the infrastructure of cold regions have also risen. To monitor the coastal erosion pattern of the circum-Arctic, earlier studies often employ spaceborne or airborne optical multi-spectral images to depict shoreline changes, which are limited by frequent clouds and haze in Arctic regions and, thus, hamper the time-series analysis. Instead, this study aims to explore the synthetic aperture radar (SAR) images, especially the recently developed microsatellite SAR data, which provide unprecedented high-resolution at a sub-meter scale, to measure the summertime spatio-temporal dynamics of an ice-rich permafrost coast along the Beaufort Sea, Alaska. The results reveal a maximum shoreline change envelope (SCE) of 64.89 m during the three-month study period. To examine the differences between the estimations and the observations derived from the conventional Sentinel-1 data, the proposed multi-stage statistical-driven scheme is used. A statistically significant positive relationship between two depicted SCEs with the presence of heteroscedasticity is confirmed. In detail, the agreement between two SCEs increases with the magnitude of the SCE, indicating that the microsatellite SAR can depict more trivial changes in coastline positions. Founded on the results and detailed discussion on the uniqueness and limitations of current SAR sensors, the promising opportunity to utilize the blooming microsatellite SAR datasets for coastal monitoring is highlighted

Convolutional neural network- based pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162690/2/mp14377.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162690/1/mp14377_am.pd

Flexible learning-free segmentation and reconstruction of neural volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively