IDCC-SAM: A Zero-Shot Approach for Cell Counting in Immunocytochemistry Dataset Using the Segment Anything Model

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Abstract

Cell counting in immunocytochemistry is vital for biomedical research, supporting the diagnosis and treatment of diseases such as neurological disorders, autoimmune conditions, and cancer. However, traditional counting methods are manual, time-consuming, and error-prone, while deep learning solutions require costly labeled datasets, limiting scalability. We introduce the Immunocytochemistry Dataset Cell Counting with Segment Anything Model (IDCC-SAM), a novel application of the Segment Anything Model (SAM), designed to adapt the model for zero-shot-based cell counting in fluorescent microscopic immunocytochemistry datasets. IDCC-SAM leverages Meta AI's SAM, pre-trained on 11 million images, to eliminate the need for annotations, enhancing scalability and efficiency. Evaluated on three public datasets (IDCIA, ADC, and VGG), IDCC-SAM achieved the lowest Mean Absolute Error (26, 28, 52) on VGG and ADC and the highest Acceptable Absolute Error (28%, 26%, 33%) across all datasets, outperforming state-of-the-art supervised models like U-Net and Mask R-CNN, as well as zero-shot benchmarks like NP-SAM and SAM4Organoid. These results demonstrate IDCC-SAM's potential to improve cell-counting accuracy while reducing reliance on specialized models and manual annotations.

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