Interactive Fluorescence Cell Counting via User-Guided Correction

OBJECTIVE: Fluorescence cell counting is vital in biomedical research, yet existing automated methods lack sufficient adaptability and accuracy, leading to persistent errors in complex microscopy images. This study aims to propose an adaptive, interactive approach to effectively overcome these limitations. METHODS: We introduce the Adaptive Interactive Cell Counting (AICC) framework, combining a coordinate-based prediction module with user-guided correction. Specifically, we develop two novel global correction algorithms, Proposal Expansion (PE) and Prediction Filtering (PF), coupled with a new RGB-Aware Structural Similarity (RGB-Aware SSIM) metric to identify visually similar regions and efficiently propagate minimal user corrections. Additionally, we release NEFCell, a new high-resolution fluorescence microscopy dataset designed explicitly for evaluating interactive cell counting methods. RESULTS: Extensive evaluations show that AICC significantly surpasses current state-of-the-art methods, reducing counting errors by up to 36.8% compared to non-interactive approaches and up to 65.3% compared to existing interactive methods, while improving localization accuracy by 7.3% on average and significantly minimizing interaction time. CONCLUSION: The proposed AICC framework substantially enhances accuracy and reduces effort required for fluorescence cell counting, proving its effectiveness in integrating automation with user expertise. SIGNIFICANCE: AICC represents a valuable tool for biomedical researchers and clinicians, facilitating precise and efficient cell analyses in complex experimental and clinical contexts.