Work In Progress Neuromorphic Cytometry Highthroughput Eventbased Flow Flowimaging

• doi: 10.1109/EBCCSP56922.2022.9845595
• title: Work in Progress: Neuromorphic Cytometry, High-throughput Event-based flow Flow-Imaging
• publisher: IEEE
• isbn: 978-1-6654-5350-9
• issn:
• rank: 4014
• access_type: LOCKED
• content_type: Conferences
• abstract: Cell sorting and counting technology has been broadly adopted for medical diagnosis, cell-based therapy, and biological research. Microscopy operates with image capture that is subject to an extremely constrained field-of-view, and even slow-moving targets may undergo motion blur, ghosting, and other movement-induced artifacts, which will ultimately degrade performance in developing machine learning models to perform cell sorting, detection, and tracking. Frame-based sensors are especially susceptible to these issues, and it is highly costly to overcome them with modern but conventional CMOS sensing technologies. We provide an early demonstration of a proof-of-concept system, with the overarching goals of curating a neuromorphic imaging cytometry (NIC) dataset, multimodal analysis techniques, and associated deep-learning models. We are working towards this goal by utilising an event-based camera to perform flow-imaging cytometry to capture cells in motion and train neural networks capable of identifying their morphology (size and shape) and identities. We propose that implementing a neuromorphic sensory system or developing a new class of event-based cameras customised for this purpose with our sorting strategy will unbind the applications from the constraints of framerate and provide a cost-efficient, reproducible and high-throughput imaging mechanism. While we target this early work for cell sorting, this novel idea is the first stepping-stone towards a new type of high-throughput and automated high-content image analysis system and screening instrument.
• article_number: 9845595
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9845595
• html_url: https://ieeexplore.ieee.org/document/9845595/
• abstract_url: https://ieeexplore.ieee.org/document/9845595/
• publication_title: 2022 8th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)
• conference_location: Krakow, Poland
• conference_dates: 22-24 June 2022
• publication_number: 9845455
• is_number: 9845499
• publication_year: 2022
• publication_date: 22-24 June 2022
• start_page: 1
• end_page: 5
• citing_paper_count: 6
• citing_patent_count: 0
• download_count: 324
• insert_date: 20220818
• index_terms:
  • ieee_terms:
    • Semiconductor device modeling
    • Target tracking
    • Neuromorphics
    • Shape
    • Neural networks
    • Morphology
    • Signal processing
  • author_terms:
    • cell sorting
    • event-based camera
    • neuromorphic camera
    • neuromorphic cell sorting
    • image-based cell sorting
  • dynamic_index_terms:
    • Neural Network
    • Cell Count
    • Cell Sorting
    • Cell Therapy
    • Cellular Therapy
    • Cell-based Therapies
    • Biological Research
    • Motion Blur
    • Neuromorphic Systems
    • Human Cells
    • High Throughput
    • Cell Morphology
    • Microparticles
    • Long Short-term Memory
    • Long Short Term Memory
    • LSTM
    • Device Performance
    • Syringe Pump
    • Target Sample
    • Microfluidic Chip
    • Lab-on-chip
    • Lab-on-a-chip
    • High-speed Camera
    • High Speed Camera
    • Optical Flow
    • Output Channels
    • Long Short-term Memory Network
    • Spiking Neural Networks
    • Spiking Neurons
    • Dynamic Vision Sensor
    • Event Camera
    • Channel Inlet
    • Narrow Field Of View
    • Narrow Field-of-view
    • Sorting System
    • Cell Sorting System
    • Cell-sorting System
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 978-1-6654-5350-9, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-6654-5349-3, isbnType: New-2005
• authors:
  • Author Name: Ziyao Zhang
    Affiliation: School of Biomedical Engineering, The University of Sydney, Australia
    Author URL: https://ieeexplore.ieee.org/author/37089494039
    ID: 37089494039
    Order: 1
    Author Affiliations:
    • School of Biomedical Engineering, The University of Sydney, Australia
  • Author Name: Maria Sabrina Ma
    Affiliation: School of Biomedical Engineering, The University of Sydney, Australia
    Author URL: https://ieeexplore.ieee.org/author/37089493634
    ID: 37089493634
    Order: 2
    Author Affiliations:
    • School of Biomedical Engineering, The University of Sydney, Australia
  • Author Name: Jason K. Eshraghian
    Affiliation: Dept. of Electrical Engineering and Computer Science, The University of Michigan, USA
    Author URL: https://ieeexplore.ieee.org/author/37085814242
    ID: 37085814242
    Order: 3
    Author Affiliations:
    • Dept. of Electrical Engineering and Computer Science, The University of Michigan, USA
  • Author Name: Daniele Vigolo
    Affiliation: School of Biomedical Engineering, The University of Sydney, Australia
    Author URL: https://ieeexplore.ieee.org/author/37089494436
    ID: 37089494436
    Order: 4
    Author Affiliations:
    • School of Biomedical Engineering, The University of Sydney, Australia
  • Author Name: Ken-Tye Yong
    Affiliation: School of Biomedical Engineering, The University of Sydney, Australia
    Author URL: https://ieeexplore.ieee.org/author/37089490175
    ID: 37089490175
    Order: 5
    Author Affiliations:
    • School of Biomedical Engineering, The University of Sydney, Australia
  • Author Name: Omid Kavehei
    Affiliation: School of Biomedical Engineering, The University of Sydney, Australia
    Author URL: https://ieeexplore.ieee.org/author/37398910100
    ID: 37398910100
    Order: 6
    Author Affiliations:
    • School of Biomedical Engineering, The University of Sydney, Australia

Image Sensor

• sensor_type: Not specified
• resolution: Not specified
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

Applications & Benefits

• cell_imaging: Cell sorting, identification, and tracking using neuromorphic imaging systems for high-throughput applications.
• benefits: Cost-efficient and high-throughput image-based cell sorting system.

Supporting Organizations

• supported_by: University of Sydney, University of Michigan

Manuscript Details

• publication_date: 22-24 June 2022

Relevancy Score

• score: 5
• missing_fields:
  • sensor_type
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise

Processed JSON Filename

request_116a906f-eb05-4363-86a7-54744a7af732-work_in_progress_neuromorphic_cytometry_highthroughput_eventbased_flow_flowimaging.json