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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.
Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.
Basic and applied papers dealing both with engineering, physics, chemistry, and computer science and with biology and medicine with respect to bio-molecules and cells. The content of acceptable papers ranges from practical/clinical/environmental applications to formalized mathematical theory. TAB #73-June 2001. (Original name-IEEE Transactions on Molecular Cellular and Tissue Engineering). T-NB publishes basic and applied research papers dealing with the study ...
Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...
3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006., 2006
To investigate the intricate nervous processes involved in many biological activities by image analysis, accurate and reproducible labeling and measurement of neurites is a prerequisite. We have developed a fully automated neurite analysis method to assist this task. Unlike most of the previous reported manual or semiautomatic methods, our approach is fully automated. Single and connected centerlines along neurites are ...
IEEE Transactions on Medical Imaging, 2016
Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. However, learning such filter banks is very time- consuming, thus limiting the amount of filters employed and the adaptation to other data sets (i.e., slow re-training). We ...
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
Tracing vasculature and neurites from teravoxel sized light-microscopy data- sets is a challenge impeding the availability of processed data to the research community. This is because (1) Holding terabytes of data during run- time is not easy for a regular PC. (2) Processing all the data at once would be slow and inefficient. In this paper, we propose a way ...
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Neurite tracing in 3D neuron images is important when it comes to analysing the growth and functionality of nerve cells. The methods used today are either of high complexity, limiting throughput, or semi-automatic, i.e., requiring user interaction. This makes them unsuitable for analysis where high throughput is needed. In this work we propose a method designed for low complexity and ...
2009 Workshop on Applications of Computer Vision (WACV), 2009
Microscope imaging technologies have turned out to yield an indispensable tool in modern biomedical research. Combined with fluorescence labeling techniques they not only provide new perspectives on tissues and cells as a whole, but also on processes at the cellular level, and will be one important experimental technique of systems biology. To handle this steadily increasing amount of image data, ...
To investigate the intricate nervous processes involved in many biological activities by image analysis, accurate and reproducible labeling and measurement of neurites is a prerequisite. We have developed a fully automated neurite analysis method to assist this task. Unlike most of the previous reported manual or semiautomatic methods, our approach is fully automated. Single and connected centerlines along neurites are extracted. The computerized method can also output branching and end points. Due to its multi-scale nature, both thick and thin neurites are simultaneously detected. With the accurate and fully automated extraction of neurite centerlines and measurement of neurite lengths, the proposed method, which greatly reduces human labor and improves efficiency, can serve as a candidate tool for large- scale neurite analysis beyond the capability of manual tracing methods
Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. However, learning such filter banks is very time- consuming, thus limiting the amount of filters employed and the adaptation to other data sets (i.e., slow re-training). We address this limitation by proposing a novel acceleration strategy to speed-up convolutional sparse coding filter learning for curvilinear structure segmentation. Our approach is based on a novel initialisation strategy (warm start), and therefore it is different from recent methods improving the optimisation itself. Our warm- start strategy is based on carefully designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear structures which are then refined by convolutional sparse coding. Experiments on four diverse data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces significantly the time taken to learn convolutional filter banks (i.e., up to -82%) compared to conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in fact, filters learned with the proposed strategy often achieve a much lower reconstruction error and match or exceed the segmentation performance of random and DCT-based initialisation, when used as input to a random forest classifier.
Tracing vasculature and neurites from teravoxel sized light-microscopy data- sets is a challenge impeding the availability of processed data to the research community. This is because (1) Holding terabytes of data during run- time is not easy for a regular PC. (2) Processing all the data at once would be slow and inefficient. In this paper, we propose a way to mitigate this challenge by Divide Conquer and Combine (DCC) method. We first split the volume into many smaller and manageable sub-volumes before tracing. These sub- volumes can then be traced individually in parallel (or otherwise). We propose an algorithm to stitch together the traced data from these sub-volumes. This algorithm is robust and handles challenging scenarios like (1) sub-optimal tracing at edges (2) densely packed structures and (3) different depths of trace termination. We validate our results using whole mouse brain vasculature data-set obtained from the Knife-Edge Scanning Microscopy (KESM) based automated tissue scanner.
Neurite tracing in 3D neuron images is important when it comes to analysing the growth and functionality of nerve cells. The methods used today are either of high complexity, limiting throughput, or semi-automatic, i.e., requiring user interaction. This makes them unsuitable for analysis where high throughput is needed. In this work we propose a method designed for low complexity and void of user interaction by using local path-finding. The method is illustrated on both phantom and real data, and compared with a widely used commercial software package with promising results.
Microscope imaging technologies have turned out to yield an indispensable tool in modern biomedical research. Combined with fluorescence labeling techniques they not only provide new perspectives on tissues and cells as a whole, but also on processes at the cellular level, and will be one important experimental technique of systems biology. To handle this steadily increasing amount of image data, in this paper we propose a new and fully automatic approach for neurite segmentation and protein quantification. Our technique combines three phases of automatic neuron cell localization, neurite segmentation and protein analysis. In the second stage active contour models based on hierarchical gradient vector flow fields are employed, enabling precise neurite segmentation despite inhomogeneous texture. Neurite segmentation results as well as protein quantification profiles from a set of test images demonstrate the appropriateness of our approach for practical biomedical research.
Engineered scaffolds simultaneously exhibiting multiple cues are highly desirable for neural tissue regeneration. To this end, we developed a neural tissue engineering scaffold that displays submicrometer-scale features, electrical conductivity, and neurotrophic activity. Specifically, electrospun poly(lactic acid-co-glycolic acid) (PLGA) nanofibers were layered with a nanometer thick coating of electrically conducting polypyrrole (PPy) presenting carboxylic groups. Then, nerve growth factor (NGF) was chemically immobilized onto the surface of the fibers. These NGF-immobilized PPy-coated PLGA (NGF-PPyPLGA) fibers supported PC12 neurite formation (28.0±3.0% of the cells) and neurite outgrowth (14.2 μm median length), which were comparable to that observed with NGF (50 ng/mL) in culture medium (29.Oil.3%, 14.4 μm). Electrical stimulation of PC12 cells on NGF-immobilized PPyPLGA fiber scaffolds was found to further improve neurite development and neurite length by 18% and 17%, respectively, compared to unstimulated cells on the NGF- immobilized fibers. Hence, submicrometer-scale fibrous scaffolds that incorporate neurotrophic and electroconducting activities may serve as promising neural tissue engineering scaffolds such as nerve guidance conduits.
We present a method for semi-automatic tracing and measuring of neurite outgrowth from time-lapse sequences of digital Nomarski micrographs. The algorithm is based on neurite ridge extraction and characterization from a single frame, followed by an automatic neurite tracking and measurement along the image sequence. Our method was tested with two sequences one containing 29 and other with 77 frames taken at intervals of 2 min. Our method rendered comparable length measurements but better time performance than measurements made by use of certain public software.
To distribute neurites (axons) along a surface and to guide them towards specific point targets we cultured spinal cord explants on coverslips printed with a micro-patterned grid of poly (ethylene imine) (PEI) lanes. The grid was prepared by micro-contact printing with silicone stamps. Spinal cord explants were resected from neonatal rats. A tiny bunch of glass filaments was used to ensure adhesion of the explant to the coverslip. One end of the bunch was glued to the coverslip while the other end pressed the explant firmly onto the coverslip. Spinal cord explants cultured in a collagen matrix, on a coverslip stamped with poly D-lysine (PDL), or on a coverslip uniformly coated with PEI or PDL were used as controls. Cultures were maintained for 6 days in vitro (div). Outgrowth from the explant was observed using phase contrast microscopy. None of the explants detached from the coverslip. Neurites emerged randomly from the explants, but upon crossing one of the grid lanes they subsequently followed the grid pattern. The outgrowing neurites were guided by the PEI lanes of the grid and reached lengths of up to 2400 /spl mu/m. After 6 div no signs of degeneration were observed in the outgrowth of explants cultured on a stamped coverslip or on a homogeneously coated coverslip, while degeneration did appear in the explants cultured in collagen after 4 div. Compared to control explants cultured on PDL (either on stamped or on uniformly coated coverslips), explants cultured on a micro-patterned PEI grid grew more profuse (more and longer neurites). Because these cultures can be easily manipulated, this paradigm is ideally suitable for studies of neuronal networks and for studies that necessitate the guidance of neurites towards a specific target (in culture), for instance the electrodes of a multi-electrode array in a culture dish.
In this work a synergistic approach is used to investigate how directional anisotropic surfaces (i.e., nanogratings) control the alignment of PC12 neurites. Finite Element models were used to assess the distribution of stresses in non-spread growth cones and filopodia. The stress field was assumed to be the main triggering cause fostering the increase and stabilization of filopodia, so the local stress maxima were directly related to the neuritic orientation. Moreover, a computational framework was implemented within an open source Java environment (CX3D), and in silico simulations were carried out to reproduce and predict biological experiments. No significant differences were found between biological experiments and in silico simulations (alignment angle, p = 0.4685; tortuosity, p = 0.9075) with a standard level of confidence (95%).
The study of the molecular mechanisms involved in neurite outgrowth and differentiation, requires essential accurate and reproducible segmentation and quantification of neuronal processes. The common method used in this study is to detect and trace individual neurites, i.e. neurite tracing. The challenge comes mainly from the morphological problem in which these images contain ambiguities such as neurites discontinuities and intensity differences. In our work, we encounter a bigger challenge as the neurites in our images have a higher density of neurites. In this paper, we present a hybrid complex coherence-enhanced method for sharpening the morphology of neurons from such images. Coherence-enhanced diffusion (CED) is used to enhance the flowlike structures of the neurites, while the imaginary part of the complex nonlinear diffusion of the image cancels the appearance of 'clouds'. We also describe an elementary method for estimating the density of neuritis based on the obtained images. Our preliminary results show that the proposed methodology is a step ahead toward an effective neuronal morphology algorithm.
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