centroid-based方法在细胞检测中更快更准，适合资源有限场景
A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology
- 用中心点回归代替分割，提升检测效率
- IFCRN在两个数据集上均优于分割模型，准确率更高
- 适合医疗影像分析中算力受限的实际情况
准确高效的细胞检测对生物医学图像分析至关重要。本研究评估了多种深度学习方法在巴氏染色细胞全切片图像（WSI）中的细胞检测性能，重点关注预测准确性和计算效率。对比了StarDist、Cellpose、SAM2等主流分割模型及基于中心点的全卷积回归网络（FCRN）方法，涵盖CNSeg和口腔癌（OC）两个数据集。提出基于真实位置距离的评估指标，并探究数据量与数据增强对模型表现的影响。结果表明，基于中心点的方法（尤其是改进的FCRN，IFCRN）在检测精度和计算效率上均优于分割方法。该研究证实中心点检测器在资源受限环境下具有优势，处理速度更快、显存占用更低，且不牺牲准确性。
原文摘要 · Abstract (English)
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.
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