A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images
Although deep learning and computer vision based approaches have demonstrated success in the field of cell counting and detection in microscopic images, they continue to have certain limitations. More specifically, they experience an overall increase in false positives when dealing with cell populat...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10802904/ |
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| author | C. Emre Dedeagac Can F. Koyuncu Michelle M. Adams Cagatay Edemen Berk C. Ugurdag N. Ilgim Ardic-Avci H. Fatih Ugurdag |
| author_facet | C. Emre Dedeagac Can F. Koyuncu Michelle M. Adams Cagatay Edemen Berk C. Ugurdag N. Ilgim Ardic-Avci H. Fatih Ugurdag |
| author_sort | C. Emre Dedeagac |
| collection | DOAJ |
| description | Although deep learning and computer vision based approaches have demonstrated success in the field of cell counting and detection in microscopic images, they continue to have certain limitations. More specifically, they experience an overall increase in false positives when dealing with cell populations that show high density and heterogeneity. Existing approaches require the reselection of parameters for each new dataset to improve the accuracy of cell counting. Therefore, it is necessary to revise the fundamental models for each new microscopic image. This study introduces a novel neural network-based method that eliminates the need for retraining by combining the pretrained Cellpose and Stardist models. The accuracy of our proposed approach was evaluated on a variety of microscopic images. Despite variations in cell densities, our proposed approach demonstrated a notably improved cell counting performance in comparison to solely utilizing the Cellpose and Stardist models. |
| format | Article |
| id | doaj-art-eed7487972eb4808938f0e6080ce4857 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eed7487972eb4808938f0e6080ce48572025-08-20T02:56:47ZengIEEEIEEE Access2169-35362024-01-011219555219556010.1109/ACCESS.2024.351764110802904A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy ImagesC. Emre Dedeagac0https://orcid.org/0000-0001-7197-9541Can F. Koyuncu1https://orcid.org/0000-0001-5990-2226Michelle M. Adams2https://orcid.org/0000-0002-5249-6461Cagatay Edemen3Berk C. Ugurdag4N. Ilgim Ardic-Avci5H. Fatih Ugurdag6https://orcid.org/0000-0002-6256-0850Department of Computer Science, Özyeğin University, İstanbul, TürkiyeDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USAInterdisciplinary Graduate Program in Neuroscience, National Nanotechnology Research Center (UNAM), Ankara, TürkiyeDepartment of Electrical and Electronics Engineering, Özyeğin University, İstanbul, TürkiyeDepartment of Computer Science, Georgetown University, Washington, DC, USAInterdisciplinary Graduate Program in Neuroscience, National Nanotechnology Research Center (UNAM), Ankara, TürkiyeDepartment of Electrical and Electronics Engineering, Özyeğin University, İstanbul, TürkiyeAlthough deep learning and computer vision based approaches have demonstrated success in the field of cell counting and detection in microscopic images, they continue to have certain limitations. More specifically, they experience an overall increase in false positives when dealing with cell populations that show high density and heterogeneity. Existing approaches require the reselection of parameters for each new dataset to improve the accuracy of cell counting. Therefore, it is necessary to revise the fundamental models for each new microscopic image. This study introduces a novel neural network-based method that eliminates the need for retraining by combining the pretrained Cellpose and Stardist models. The accuracy of our proposed approach was evaluated on a variety of microscopic images. Despite variations in cell densities, our proposed approach demonstrated a notably improved cell counting performance in comparison to solely utilizing the Cellpose and Stardist models.https://ieeexplore.ieee.org/document/10802904/Cell countingcell detectiondeep learningensemble learning |
| spellingShingle | C. Emre Dedeagac Can F. Koyuncu Michelle M. Adams Cagatay Edemen Berk C. Ugurdag N. Ilgim Ardic-Avci H. Fatih Ugurdag A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images IEEE Access Cell counting cell detection deep learning ensemble learning |
| title | A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images |
| title_full | A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images |
| title_fullStr | A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images |
| title_full_unstemmed | A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images |
| title_short | A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images |
| title_sort | guided ensembling approach for cell counting in fluorescence microscopy images |
| topic | Cell counting cell detection deep learning ensemble learning |
| url | https://ieeexplore.ieee.org/document/10802904/ |
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