A new algorithm to split and merge ultra-high resolution 3D images

Splitting and merging ultra-high resolution 3D images is a requirement for parallel or distributed processing operations. Naive algorithms to split and merge 3D blocks from ultra-high resolution images perform very poorly, due to the number of seeks required to reconstruct spatially-adjacent blocks from linear data organizations on disk. The current solution to deal with this problem is to use file formats that preserve spatial proximity on disk, but this comes with additional complexity. We introduce a new algorithm called Multiple reads/writes to split and merge ultra-high resolution 3D images efficiently from simple file formats. Multiple reads/writes only access contiguous bytes in the reconstructed image, which leads to substantial performance improvements compared to existing algorithms. We parallelize our algorithm using multi-threading, which further improves the performance for data stored on a Hadoop cluster. We also show that on-the-fly lossless compression with the lz4 algorithm reduces the split and merge time further

Cross-Talk-Free Multi-Color STORM Imaging Using a Single Fluorophore

Multi-color stochastic optical reconstruction microscopy (STORM) is routinely performed; however, the various approaches for achieving multiple colors have important caveats. Color cross-talk, limited availability of spectrally distinct fluorophores with optimal brightness and duty cycle, incompatibility of imaging buffers for different fluorophores, and chromatic aberrations impact the spatial resolution and ultimately the number of colors that can be achieved. We overcome these complexities and develop a simple approach for multi-color STORM imaging using a single fluorophore and sequential labelling. In addition, we present a simple and versatile method to locate the same region of interest on different days and even on different microscopes. In combination, these approaches enable cross-talk-free multi-color imaging of sub-cellular structures.Peer ReviewedPostprint (published version

Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey

Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D and 3D imaging techniques. Due to the laborious and time-consuming nature of manual segmentation of large-scale EM datasets, automated segmentation approaches are crucial. This review focuses on the progress of deep learning-based segmentation techniques in large-scale cellular EM throughout the last six years, during which significant progress has been made in both semantic and instance segmentation. A detailed account is given for the key datasets that contributed to the proliferation of deep learning in 2D and 3D EM segmentation. The review covers supervised, unsupervised, and self-supervised learning methods and examines how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images. The special challenges posed by such images, like heterogeneity and spatial complexity, and the network architectures that overcame some of them are described. Moreover, an overview of the evaluation measures used to benchmark EM datasets in various segmentation tasks is provided. Finally, an outlook of current trends and future prospects of EM segmentation is given, especially with large-scale models and unlabeled images to learn generic features across EM datasets

Parallel 3D Fast Wavelet Transform comparison on CPUs and GPUs

We present in this paper several implementations of the 3D Fast Wavelet Transform (3D-FWT) on multicore CPUs and manycore GPUs. On the GPU side, we focus on CUDA and OpenCL programming to develop methods for an efficient mapping on manycores. On multicore CPUs, OpenMP and Pthreads are used as counterparts to maximize parallelism, and renowned techniques like tiling and blocking are exploited to optimize the use of memory. We evaluate these proposals and make a comparison between a new Fermi Tesla C2050 and an Intel Core 2 QuadQ6700. Speedups of the CUDA version are the best results, improving the execution times on CPU, ranging from 5.3x to 7.4x for different image sizes, and up to 81 times faster when communications are neglected. Meanwhile, OpenCL obtains solid gains which range from 2x factors on small frame sizes to 3x factors on larger ones

Time domain based image generation for synthetic aperture radar on field programmable gate arrays

Aerial images are important in different scenarios including surface cartography, surveillance, disaster control, height map generation, etc. Synthetic Aperture Radar (SAR) is one way to generate these images even through clouds and in the absence of daylight. For a wide and easy usage of this technology, SAR systems should be small, mounted to Unmanned Aerial Vehicles (UAVs) and process images in real-time. Since UAVs are small and lightweight, more robust (but also more complex) time-domain algorithms are required for good image quality in case of heavy turbulence. Typically the SAR data set size does not allow for ground transmission and processing, while the UAV size does not allow for huge systems and high power consumption to process the data. A small and energy-efficient signal processing system is therefore required. To fill the gap between existing systems that are capable of either high-speed processing or low power consumption, the focus of this thesis is the analysis, design, and implementation of such a system. A survey shows that most architectures either have to high power budgets or too few processing capabilities to match real-time requirements for time-domain-based processing. Therefore, a Field Programmable Gate Array (FPGA) based system is designed, as it allows for high performance and low-power consumption. The Global Backprojection (GBP) is implemented, as it is the standard time-domain-based algorithm which allows for highest image quality at arbitrary trajectories at the complexity of O(N3). To satisfy real-time requirements under all circumstances, the accelerated Fast Factorized Backprojection (FFBP) algorithm with a complexity of O(N2logN) is implemented as well, to allow for a trade-off between image quality and processing time. Additionally, algorithm and design are enhanced to correct the failing assumptions for Frequency Modulated Continuous Wave (FMCW) Radio Detection And Ranging (Radar) data at high velocities. Such sensors offer high-resolution data at considerably low transmit power which is especially interesting for UAVs. A full analysis of all algorithms is carried out, to design a highly utilized architecture for maximum throughput. The process covers the analysis of mathematical steps and approximations for hardware speedup, the analysis of code dependencies for instruction parallelism and the analysis of streaming capabilities, including memory access and caching strategies, as well as parallelization considerations and pipeline analysis. Each architecture is described in all details with its surrounding control structure. As proof of concepts, the architectures are mapped on a Virtex 6 FPGA and results on resource utilization, runtime and image quality are presented and discussed. A special framework allows to scale and port the design to other FPGAs easily and to enable for maximum resource utilization and speedup. The result is streaming architectures that are capable of massive parallelization with a minimum in system stalls. It is shown that real-time processing on FPGAs with strict power budgets in time-domain is possible with the GBP (mid-sized images) and the FFBP (any image size with a trade-off in quality), allowing for a UAV scenario

A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation

We propose a novel theoretical framework that generalizes algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning, and allows us to explore many combinations of different linkage criteria and cannot-link constraints. We prove the equivalence of existing clustering methods to some of those combinations, and introduce new algorithms for combinations which have not been studied. An extensive comparison is performed to evaluate properties of the clustering algorithms in the context of instance segmentation in images, including robustness to noise and efficiency. We show how one of the new algorithms proposed in our framework outperforms all previously known agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM segmentation benchmark and on the CityScapes dataset.Comment: 19 pages, 8 figures, 6 table

Real-time tomographic reconstruction

With tomography it is possible to reconstruct the interior of an object without destroying. It is an important technique for many applications in, e.g., science, industry, and medicine. The runtime of conventional reconstruction algorithms is typically much longer than the time it takes to perform the tomographic experiment, and this prohibits the real-time reconstruction and visualization of the imaged object. The research in this dissertation introduces various techniques such as new parallelization schemes, data partitioning methods, and a quasi-3D reconstruction framework, that significantly reduce the time it takes to run conventional tomographic reconstruction algorithms without affecting image quality. The resulting methods and software implementations put reconstruction times in the same ballpark as the time it takes to do a tomographic scan, so that we can speak of real-time tomographic reconstruction.NWONumber theory, Algebra and Geometr

Joint Visual and Wireless Tracking System

Object tracking is an important component in many applications including surveillance, manufacturing, inventory tracking, etc. The most common approach is to combine a surveillance camera with an appearance-based visual tracking algorithm. While this approach can provide high tracking accuracy, the tracker can easily diverge in environments where there are much occlusions. In recent years, wireless tracking systems based on different frequency ranges are becoming more popular. While systems using ultra-wideband frequencies suffer similar problems as visual systems, there are systems that use frequencies as low as in those in the AM band to circumvent the problems of obstacles, and exploit the near-field properties between the electric and magnetic waves to achieve tracking accuracy down to about one meter. In this dissertation, I study the combination of a visual tracker and a low-frequency wireless tracker to improve visual tracking in highly occluded area. The proposed system utilizes two homographies formed between the world coordinates with the image coordinates of the head and the foot of the target person. Using the world coordinate system, the proposed system combines a visual tracker and a wireless tracker in an Extended Kalman Filter framework for joint tracking. Extensive experiments have been conducted using both simulations and real videos to demonstrate the validity of our proposed scheme