Next Article in Journal
Design and Validation of a PLC-Controlled Morbidostat for Investigating Bacterial Drug Resistance
Previous Article in Journal
Mathematical Modeling of the Gastrointestinal System for Preliminary Drug Absorption Assessment
Previous Article in Special Issue
Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

CellRegNet: Point Annotation-Based Cell Detection in Histopathological Images via Density Map Regression

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
(This article belongs to the Special Issue Artificial Intelligence-Based Diagnostics and Biomedical Analytics)

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.
Keywords: digital pathology; cell detection; attention mechanism digital pathology; cell detection; attention mechanism

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

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop