Using optimal transport theory to optimize a deep convolutional neural network microscopic cell counting method.
Cell counting
Convolutional neural network model
Density regression
Optimal transport
Journal
Medical & biological engineering & computing
ISSN: 1741-0444
Titre abrégé: Med Biol Eng Comput
Pays: United States
ID NLM: 7704869
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
19
09
2022
accepted:
17
05
2023
medline:
23
10
2023
pubmed:
3
8
2023
entrez:
2
8
2023
Statut:
ppublish
Résumé
Medical image processing has become increasingly important in recent years, particularly in the field of microscopic cell imaging. However, accurately counting the number of cells in an image can be a challenging task due to the significant variations in cell size and shape. To tackle this problem, many existing methods rely on deep learning techniques, such as convolutional neural networks (CNNs), to count cells in an image or use regression counting methods to learn the similarities between an input image and a predicted cell image density map. In this paper, we propose a novel approach to monitor the cell counting process by optimizing the loss function using the optimal transport method, a rigorous measure to calculate the difference between the predicted count map and the dot annotation map generated by the CNN. We evaluated our algorithm on three publicly available cell count benchmarks: the synthetic fluorescence microscopy (VGG) dataset, the modified bone marrow (MBM) dataset, and the human subcutaneous adipose tissue (ADI) dataset. Our method outperforms other state-of-the-art methods, achieving a mean absolute error (MAE) of 2.3, 4.8, and 13.1 on the VGG, MBM, and ADI datasets, respectively, with smaller standard deviations. By using the optimal transport method, our approach provides a more accurate and reliable cell counting method for medical image processing.
Identifiants
pubmed: 37532907
doi: 10.1007/s11517-023-02862-7
pii: 10.1007/s11517-023-02862-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2939-2950Subventions
Organisme : National Natural Science Foundation of China
ID : 81871508
Organisme : National Natural Science Foundation of China
ID : 61773246
Organisme : China Postdoctoral Science Foundation
ID : No. 2021M691982
Organisme : Taishan Scholar Program of Shandong Province of China
ID : TSHW201502038
Informations de copyright
© 2023. International Federation for Medical and Biological Engineering.
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