52 research outputs found

    Neuron Segmentation Using Deep Complete Bipartite Networks

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    In this paper, we consider the problem of automatically segmenting neuronal cells in dual-color confocal microscopy images. This problem is a key task in various quantitative analysis applications in neuroscience, such as tracing cell genesis in Danio rerio (zebrafish) brains. Deep learning, especially using fully convolutional networks (FCN), has profoundly changed segmentation research in biomedical imaging. We face two major challenges in this problem. First, neuronal cells may form dense clusters, making it difficult to correctly identify all individual cells (even to human experts). Consequently, segmentation results of the known FCN-type models are not accurate enough. Second, pixel-wise ground truth is difficult to obtain. Only a limited amount of approximate instance-wise annotation can be collected, which makes the training of FCN models quite cumbersome. We propose a new FCN-type deep learning model, called deep complete bipartite networks (CB-Net), and a new scheme for leveraging approximate instance-wise annotation to train our pixel-wise prediction model. Evaluated using seven real datasets, our proposed new CB-Net model outperforms the state-of-the-art FCN models and produces neuron segmentation results of remarkable qualityComment: miccai 201

    Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold

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    Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipments. PD direction of arrival (DOA) algorithms using conventional beamforming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, which make them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then according to the positioning requirements, the antenna array and its arrangement are developed, optimized, and simulation results suggested that the algorithm has reliable direction-finding performance in the form of 6 elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is 3.39{\deg}, which can meet the need for Partial discharge DOA estimation using inspection robots in substations.Comment: 8 pages,13 figures,24 reference

    EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation

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    Artificial intelligence (AI) has been widely used in bioimage image analysis nowadays, but the efficiency of AI models, like the energy consumption and latency is not ignorable due to the growing model size and complexity, as well as the fast-growing analysis needs in modern biomedical studies. Like we can compress large images for efficient storage and sharing, we can also compress the AI models for efficient applications and deployment. In this work, we present EfficientBioAI, a plug-and-play toolbox that can compress given bioimaging AI models for them to run with significantly reduced energy cost and inference time on both CPU and GPU, without compromise on accuracy. In some cases, the prediction accuracy could even increase after compression, since the compression procedure could remove redundant information in the model representation and therefore reduce over-fitting. From four different bioimage analysis applications, we observed around 2-5 times speed-up during inference and 30-80%\% saving in energy. Cutting the runtime of large scale bioimage analysis from days to hours or getting a two-minutes bioimaging AI model inference done in near real-time will open new doors for method development and biomedical discoveries. We hope our toolbox will facilitate resource-constrained bioimaging AI and accelerate large-scale AI-based quantitative biological studies in an eco-friendly way, as well as stimulate further research on the efficiency of bioimaging AI.Comment: 17 pages, 6 figure

    Development of a Non-invasive Deep Brain Stimulator With Precise Positioning and Real-Time Monitoring of Bioimpedance

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    Methods by which to achieve non-invasive deep brain stimulation via temporally interfering with electric fields have been proposed, but the precision of the positioning of the stimulation and the reliability and stability of the outputs require improvement. In this study, a temporally interfering electrical stimulator was developed based on a neuromodulation technique using the interference modulation waveform produced by several high-frequency electrical stimuli to treat neurodegenerative diseases. The device and auxiliary software constitute a non-invasive neuromodulation system. The technical problems related to the multichannel high-precision output of the device were solved by an analog phase accumulator and a special driving circuit to reduce crosstalk. The function of measuring bioimpedance in real time was integrated into the stimulator to improve effectiveness. Finite element simulation and phantom measurements were performed to find the functional relations among the target coordinates, current ratio, and electrode position in the simplified model. Then, an appropriate approach was proposed to find electrode configurations for desired target locations in a detailed and realistic mouse model. A mouse validation experiment was carried out under the guidance of a simulation, and the reliability and positioning accuracy of temporally interfering electric stimulators were verified. Stimulator improvement and precision positioning solutions promise opportunities for further studies of temporally interfering electrical stimulation

    Cathelicidin-BF, a Snake Cathelicidin-Derived Antimicrobial Peptide, Could Be an Excellent Therapeutic Agent for Acne Vulgaris

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    Cathelicidins are a family of antimicrobial peptides acting as multifunctional effector molecules in innate immunity. Cathelicidin-BF has been purified from the snake venoms of Bungarus fasciatus and it is the first identified cathelicidin antimicrobial peptide in reptiles. In this study, cathelicidin-BF was found exerting strong antibacterial activities against Propionibacterium acnes. Its minimal inhibitory concentration against two strains of P. acnes was 4.7 ”g/ml. Cathelicidin-BF also effectively killed other microorganisms including Staphylococcus epidermidis, which was possible pathogen for acne vulgaris. Cathelicidin-BF significantly inhibited pro-inflammatory factors secretion in human monocytic cells and P. acnes-induced O2.− production of human HaCaT keratinocyte cells. Observed by scanning electron microscopy, the surfaces of the treated pathogens underwent obvious morphological changes compared with the untreated controls, suggesting that this antimicrobial peptide exerts its action by disrupting membranes of microorganisms. The efficacy of cathelicidin-BF gel topical administering was evaluated in experimental mice skin colonization model. In vivo anti-inflammatory effects of cathelicidin-BF were confirmed by relieving P. acnes-induced mice ear swelling and granulomatous inflammation. The anti-inflammatory effects combined with potent antimicrobial activities and O2.− production inhibition activities of cathelicidin-BF indicate its potential as a novel therapeutic option for acne vulgaris

    Understanding metric-related pitfalls in image analysis validation

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    Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

    Phytoremediation of heavy metal-contaminated sites: Eco-environmental concerns, field studies, sustainability issues and future prospects

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    Environmental contamination due to heavy metals (HMs) is of serious ecotoxicological concern worldwide because of their increasing use at industries. Due to non-biodegradable and persistent nature, HMs cause serious soil/water pollution and severe health hazards in living beings upon exposure. HMs can be genotoxic, carcinogenic, mutagenic, and teratogenic in nature even at low concentration. They may also act as endocrine disruptors and induce developmental as well as neurological disorders and thus, their removal from our natural environment is crucial for the rehabilitation of contaminated sites. To cope with HM pollution, phytoremediation has emerged as a low-cost and eco-sustainable solution to conventional physico-chemical cleanup methods that require high capital investment and labor alter soil properties and disturb soil microflora. Phytoremediation is a green technology wherein plants and associated microbes are used to remediate HM-contaminated sites to safeguard the environment and protect public health. Hence, in view of the above, the present paper aims to examine the feasibility of phytoremediation as a sustainable remediation technology for the management of metals-contaminated sites. Therefore, this paper provides an in-depth review on both the conventional and novel phytoremediation approaches, evaluate their efficacy to remove toxic metals from our natural environment, explore current scientific progresses, field experiences and sustainability issues and revise world over trends in phytoremediation research for its wider recognition and public acceptance as a sustainable remediation technology for the management of contaminated sites in 21st century
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