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Open AccessArticle
CellRegNet: Point Annotation-Based Cell Detection in Histopathological Images via Density Map Regression
by
Xu Jin
Xu Jin
,
Hong An
Hong An *
and
Mengxian Chi
Mengxian Chi
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
*
Author to whom correspondence should be addressed.
Bioengineering 2024, 11(8), 814; https://doi.org/10.3390/bioengineering11080814 (registering DOI)
Submission received: 15 July 2024
/
Revised: 5 August 2024
/
Accepted: 8 August 2024
/
Published: 10 August 2024
Abstract
Recent advances in deep learning have shown significant potential for accurate cell detection via density map regression using point annotations. However, existing deep learning models often struggle with multi-scale feature extraction and integration in complex histopathological images. Moreover, in multi-class cell detection scenarios, current density map regression methods typically predict each cell type independently, failing to consider the spatial distribution priors of different cell types. To address these challenges, we propose CellRegNet, a novel deep learning model for cell detection using point annotations. CellRegNet integrates a hybrid CNN/Transformer architecture with innovative feature refinement and selection mechanisms, addressing the need for effective multi-scale feature extraction and integration. Additionally, we introduce a contrastive regularization loss that models the mutual exclusiveness prior in multi-class cell detection cases. Extensive experiments on three histopathological image datasets demonstrate that CellRegNet outperforms existing state-of-the-art methods for cell detection using point annotations, with F1-scores of 86.38% on BCData (breast cancer), 85.56% on EndoNuke (endometrial tissue) and 93.90% on MBM (bone marrow cells), respectively. These results highlight CellRegNet’s potential to enhance the accuracy and reliability of cell detection in digital pathology.
Share and Cite
MDPI and ACS Style
Jin, X.; An, H.; Chi, M.
CellRegNet: Point Annotation-Based Cell Detection in Histopathological Images via Density Map Regression. Bioengineering 2024, 11, 814.
https://doi.org/10.3390/bioengineering11080814
AMA Style
Jin X, An H, Chi M.
CellRegNet: Point Annotation-Based Cell Detection in Histopathological Images via Density Map Regression. Bioengineering. 2024; 11(8):814.
https://doi.org/10.3390/bioengineering11080814
Chicago/Turabian Style
Jin, Xu, Hong An, and Mengxian Chi.
2024. "CellRegNet: Point Annotation-Based Cell Detection in Histopathological Images via Density Map Regression" Bioengineering 11, no. 8: 814.
https://doi.org/10.3390/bioengineering11080814
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