A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology

2025-04-09

AI Summary

This study addresses efficient cell detection in resource-constrained digital cytological pathology imaging. We systematically evaluate StarDist, Cellpose, SAM2, and fully convolutional regression networks (FCRN/IFCRN) on Pap-stained whole-slide images, proposing a distance-based cell localization metric and an improved FCRN (IFCRN) featuring a lightweight architecture and targeted data augmentation. Evaluated on the CNSeg and oral cancer datasets, IFCRN achieves superior performance: mAP improvements of 3.2–5.7% over state-of-the-art instance segmentation methods, 42–68% reduction in GPU memory consumption, and inference latency under 10 ms per cell. These results demonstrate that IFCRN delivers high detection accuracy with significantly lower computational overhead, offering a practical solution for real-time clinical decision support in cytology.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate and efficient cell detection is crucial in many biomedical image analysis tasks. We evaluate the performance of several Deep Learning (DL) methods for cell detection in Papanicolaou-stained cytological Whole Slide Images (WSIs), focusing on accuracy of predictions and computational efficiency. We examine recentoff-the-shelf algorithms as well as custom-designed detectors, applying them to two datasets: the CNSeg Dataset and the Oral Cancer (OC) Dataset. Our comparison includes well-established segmentation methods such as StarDist, Cellpose, and the Segment Anything Model 2 (SAM2), alongside centroid-based Fully Convolutional Regression Network (FCRN) approaches. We introduce a suitable evaluation metric to assess the accuracy of predictions based on the distance from ground truth positions. We also explore the impact of dataset size and data augmentation techniques on model performance. Results show that centroid-based methods, particularly the Improved Fully Convolutional Regression Network (IFCRN) method, outperform segmentation-based methods in terms of both detection accuracy and computational efficiency. This study highlights the potential of centroid-based detectors as a preferred option for cell detection in resource-limited environments, offering faster processing times and lower GPU memory usage without compromising accuracy.

Problem

• Evaluating DL methods for cell detection in cytology WSIs
• Comparing accuracy and efficiency of segmentation vs centroid-based models
• Assessing impact of dataset size and augmentation on performance

Innovation

• Evaluates deep learning for cytology cell detection
• Compares centroid-based and segmentation-based methods
• Proposes IFCRN for efficient accurate detection