Enhancing Fluorescence Image Analysis Through Deep Learning

• doi: 10.1109/MetroXRAINE58569.2023.10405797
• title: Enhancing Fluorescence Image Analysis through Deep Learning
• publisher: IEEE
• isbn: 979-8-3503-0081-9
• issn:
• rank: 2401
• access_type: LOCKED
• content_type: Conferences
• abstract: Fluorescence imaging plays a crucial role in studying biological processes and materials across various industrial applications. However, the manual analysis of fluorescence images is time-consuming and prone to errors. To address these challenges, we propose novel machine learning-based approaches to enhance the VIDAS® device - an automated immunoassay system employed for medical condition detection. The current setup utilizes a photodiode for luminescence capture, which exhibits limitations when applied to spatially distributed signals. To overcome this limitation, we explore the use of a CMOS sensor to capture two-dimensional images of cuvettes, enabling a more comprehensive analysis of the system. Our proposed solution involves generating reconstructed images that rectify potential defects, leading to improved and unbiased fluorescence estimation. Through extensive experimentation, we demonstrate that employing the reconstructed images enables more accurate measurements, particularly in the presence of defects. Our methodology encompasses deep learning and semantic segmen-tation techniques, allowing robust fluorescence image analysis.
• article_number: 10405797
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10405797
• html_url: https://ieeexplore.ieee.org/document/10405797/
• abstract_url: https://ieeexplore.ieee.org/document/10405797/
• publication_title: 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
• conference_location: Milano, Italy
• conference_dates: 25-27 Oct. 2023
• publication_number: 10405419
• is_number: 10405547
• publication_year: 2023
• publication_date: 25-27 Oct. 2023
• start_page: 212
• end_page: 217
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 43
• insert_date: 20240201
• index_terms:
  • ieee_terms:
    • Atmospheric measurements
    • Pipelines
    • Imaging
    • Fluorescence
    • Particle measurements
    • Noise measurement
    • Image reconstruction
  • author_terms:
    • Fluorescence image analysis
    • Deep Learning
    • Semantic Segmentation
  • dynamic_index_terms:
    • Image Analysis
    • Fluorescence Image Analysis
    • Accurate Measurement
    • Semantic Segmentation
    • Presence Of Defects
    • Presence Of Faults
    • Neural Network
    • Convolutional Neural Network
    • Validation Set
    • Computer Vision
    • Line In Fig
    • Air Bubbles
    • Gas Bubbles
    • Part Of The Image
    • Final Measurement
    • Semantic Network
    • Working Points
    • Segmentation Dataset
    • Absence Of Defects
    • Absence Of Faults
    • Computer Vision Techniques
    • Semantic Segmentation Network
    • Compensation Algorithm
    • Segmentation Output
    • Dice Loss
    • Average Brightness
    • Defect Region
    • Fault Region
    • Impact Of Elements
    • Impact Of These Elements
    • Learning Algorithms
    • Machine Learning Algorithms
    • Neural Network For Segmentation
    • Specific Algorithm
    • Influence Of Elements
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 979-8-3503-0081-9, isbnType: New-2005
  • format: USB ISBN, value: 979-8-3503-0079-6, isbnType: New-2005
  • format: Electronic ISBN, value: 979-8-3503-0080-2, isbnType: New-2005
• authors:
  • Author Name: Paolo Andreini
    Affiliation: DIISM University of Siena, Siena, Italy
    Author URL: https://ieeexplore.ieee.org/author/751371370903765
    ID: 751371370903765
    Order: 1
    Author Affiliations:
    • DIISM University of Siena, Siena, Italy
  • Author Name: Simone Bonechi
    Affiliation: DISPOC, DIISM University of Siena, Siena, Italy
    Author URL: https://ieeexplore.ieee.org/author/37088717374
    ID: 37088717374
    Order: 2
    Author Affiliations:
    • DISPOC, DIISM University of Siena, Siena, Italy
  • Author Name: Alessandro Mecocci
    Affiliation: DIISM University of Siena, Siena, Italy
    Author URL: https://ieeexplore.ieee.org/author/37294807500
    ID: 37294807500
    Order: 3
    Author Affiliations:
    • DIISM University of Siena, Siena, Italy
  • Author Name: Veronica Lucia Rossi
    Affiliation: bioMeriéux Italia, Florence, Italy
    Author URL: https://ieeexplore.ieee.org/author/413165029613956
    ID: 413165029613956
    Order: 4
    Author Affiliations:
    • bioMeriéux Italia, Florence, Italy
  • Author Name: Giuseppe Ferorelli
    Affiliation: bioMeriéux Italia, Florence, Italy
    Author URL: https://ieeexplore.ieee.org/author/646816468705140
    ID: 646816468705140
    Order: 5
    Author Affiliations:
    • bioMeriéux Italia, Florence, Italy
  • Author Name: Giorgio Chini
    Affiliation: bioMeriéux Italia, Florence, Italy
    Author URL: https://ieeexplore.ieee.org/author/939601198172588
    ID: 939601198172588
    Order: 6
    Author Affiliations:
    • bioMeriéux Italia, Florence, Italy
  • Author Name: Antonio Sanesi
    Affiliation: bioMeriéux Italia, Florence, Italy
    Author URL: https://ieeexplore.ieee.org/author/915910575320457
    ID: 915910575320457
    Order: 7
    Author Affiliations:
    • bioMeriéux Italia, Florence, Italy

Image Sensor

• sensor_type: CMOS
• resolution: 1024x768
• 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: The study enhances fluorescence imaging which can be applied for medical and biological imaging.
• benefits: Improved accuracy and reliability of fluorescence measurements through segmentation and defect compensation.

Supporting Organizations

• supported_by: University of Siena, bioMérieux Italia

Manuscript Details

• publication_date: 25-27 Oct. 2023

Relevancy Score

• score: 8
• missing_fields:
  • 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_1ffbac23-c0b0-4e04-b225-7afd0059fe88-enhancing_fluorescence_image_analysis_through_deep_learning.json