Alignment of large-scale serial-section electron microscopy (ssEM) images is crucial for successful analysis in nano-scale connectomics. Despite various image registration algorithms proposed in the ...past, large-scale ssEM alignment remains challenging due to the size and complex nature of the data. Recently, the application of unsupervised machine learning in medical image registration has shown promise in efforts to replace an expensive numerical computation process with a once-deployed feed-forward neural network. However, the anisotropy in most ssEM data makes it difficult to directly adopt such learning-based methods for the registration of these images. Here, we propose a novel deformable image registration approach based on weakly supervised learning that can be applied to registering ssEM images at scale. The proposed method leverages slice interpolation to improve registration between images with sudden and large structural changes. In addition, the proposed method only requires roughly aligned data for training the interpolation network while the deformation network can be trained in an unsupervised fashion. We demonstrate the efficacy of the method on real ssEM datasets.
Solid-state nanopores are a nanofluidic platform with unique advantages for single-molecule analysis and filtration applications. However, significant improvements in device performance and scalable ...fabrication methods are needed to make nanopore devices competitive with existing technologies. This dissertation investigates the potential advantages of ultra-thin nanopores in which the thickness of the membrane is significantly smaller than the nanopore diameter. Novel, scalable fabrication methods were first developed and then utilized to examine device performance for water filtration and single molecule sensing applications. Fabrication of nanometer-thin pores in silicon nitride membranes was achieved using a feedback-controlled ion beam method in which ion sputtering is arrested upon detection of the first few ions that drill through the membrane. Performing fabrication at liquid nitrogen temperatures prevents surface atom rearrangements that have previously complicated similar processes. A novel cross-sectional imaging method was also developed to allow careful examination of the full nanopore geometry. Atomically-thin graphene nanopores were fabricated via an electrical pulse method in which sub-microsecond electrical pulses applied across a graphene membrane in electrolyte solution are used to create a defect in the membrane and controllably enlarge it into a nanopore. This method dramatically increases the accuracy and reliability of graphene nanopore production, allowing consistent production of single nanopores down to subnanometer sizes. In filtration applications in which nanopores are used to selectively restrict the passage of dissolved contaminants, ultra-thin nanopores minimize the flow resistance, increasing throughput and energy-efficiency. The ability of graphene nanopores to separate different ions was characterized via ionic conductance and reversal potential measurements. Graphene nanopores were observed to conduct cations preferentially over anions with selectivity ratios of 100 or higher for pores as large as 20 nm in diameter, suggesting that porous graphene membranes can be used to create highly effective cation exchange membranes for electrodialysis filtration. These surprisingly high selectivities cannot be explained by current models of ionic conduction in graphene nanopores, motivating the development of a new model in which elevated concentrations of mobile cations near the graphene surface generate additional ion selectivity.
Magnetic fields are proposed to have played a critical role in some of the most enigmatic processes of planetary formation by mediating the rapid accretion of disk material onto the central star and ...the formation of the first solids. However, there have been no experimental constraints on the intensity of these fields. Here we show that dusty olivine-bearing chondrules from the Semarkona meteorite were magnetized in a nebular field of 54 ± 21 microteslas. This intensity supports chondrule formation by nebular shocks or planetesimal collisions rather than by electric currents, the x-wind, or other mechanisms near the Sun. This implies that background magnetic fields in the terrestrial planet-forming region were likely 5 to 54 microteslas, which is sufficient to account for measured rates of mass and angular momentum transport in protoplanetary disks.
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function. In connectomics, this has been the sole purview of volume electron ...microscopy (EM), which entails an excruciatingly difficult process because it requires cutting tissue into many thin, fragile slices that then need to be imaged, aligned, and reconstructed. Unlike EM, hard X-ray imaging is compatible with thick tissues, eliminating the need for thin sectioning, and delivering fast acquisition, intrinsic alignment, and isotropic resolution. Unfortunately, current state-of-the-art X-ray microscopy provides much lower resolution, to the extent that segmenting membranes is very challenging. We propose an uncertainty-aware 3D reconstruction model that translates X-ray images to EM-like images with enhanced membrane segmentation quality, showing its potential for developing simpler, faster, and more accurate X-ray based connectomics pipelines.
The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of ...circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking. Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here. In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations.
Solid-state nanopores are a nanofluidic platform with unique advantages for single-molecule analysis and filtration applications. However, significant improvements in device performance and scalable ...fabrication methods are needed to make nanopore devices competitive with existing technologies. This dissertation investigates the potential advantages of ultra-thin nanopores in which the thickness of the membrane is significantly smaller than the nanopore diameter. Novel, scalable fabrication methods were first developed and then utilized to examine device performance for water filtration and single molecule sensing applications.
Fabrication of nanometer-thin pores in silicon nitride membranes was achieved using a feedback-controlled ion beam method in which ion sputtering is arrested upon detection of the first few ions that drill through the membrane. Performing fabrication at liquid nitrogen temperatures prevents surface atom rearrangements that have previously complicated similar processes. A novel cross-sectional imaging method was also developed to allow careful examination of the full nanopore geometry.
Atomically-thin graphene nanopores were fabricated via an electrical pulse method in which sub-microsecond electrical pulses applied across a graphene membrane in electrolyte solution are used to create a defect in the membrane and controllably enlarge it into a nanopore. This method dramatically increases the accuracy and reliability of graphene nanopore production, allowing consistent production of single nanopores down to subnanometer sizes.
In filtration applications in which nanopores are used to selectively restrict the passage of dissolved contaminants, ultra-thin nanopores minimize the flow resistance, increasing throughput and energy-efficiency. The ability of graphene nanopores to separate different ions was characterized via ionic conductance and reversal potential measurements. Graphene nanopores were observed to conduct cations preferentially over anions with selectivity ratios of 100 or higher for pores as large as 20 nm in diameter, suggesting that porous graphene membranes can be used to create highly effective cation exchange membranes for electrodialysis filtration. These surprisingly high selectivities cannot be explained by current models of ionic conduction in graphene nanopores, motivating the development of a new model in which elevated concentrations of mobile cations near the graphene surface generate additional ion selectivity.
Engineering and Applied Sciences - Applied Physics
We propose an asymmetrically cyclic adversarial network that performs denoising tasks to improve electron microscopy (EM) image analysis. Deep learning-based denoising methods have typically been ...trained either with matching pairs of noise-free and noise-corrupted images or by leveraging prior knowledge of noise distributions. Neither of these options is feasible in high-throughput EM imaging pipelines. Our proposed denoising method employs independently acquired noise-free, noise pattern, and noise-corrupted images to automatically learn the underlying noise model and generate denoised outputs. This method is based on three-way cyclic constraints with adversarial training of a deep network to improve the quality of acquired images without paired training data. Its utility is demonstrated for cases where imaging substrates add noise and where acquisition conditions contribute noise. We show that our method, which builds on the concept of CycleGAN, outperforms the current state-of-the-art denoising approaches Noise2Noise and Noise2Void, as well as other learning-based techniques.
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function. In connectomics, this has been the sole purview of volume electron ...microscopy (EM), which entails an excruciatingly difficult process because it requires cutting tissue into many thin, fragile slices that then need to be imaged, aligned, and reconstructed. Unlike EM, hard X-ray imaging is compatible with thick tissues, eliminating the need for thin sectioning, and delivering fast acquisition, intrinsic alignment, and isotropic resolution. Unfortunately, current state-of-the-art X-ray microscopy provides much lower resolution, to the extent that segmenting membranes is very challenging. We propose an uncertainty-aware 3D reconstruction model that translates X-ray images to EM-like images with enhanced membrane segmentation quality, showing its potential for developing simpler, faster, and more accurate X-ray based connectomics pipelines.
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