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IDCC-SAM: A Zero-Shot Approach for Cell Counting in Immunocytochemistry Dataset Using the Segment Anything Model
Samuel Fanijo1, Ali Jannesari1, Julie Dickerson2
1Department of Computer Science, Iowa State University, Ames, IA 50010, USA.
Insights
We developed IDCC-SAM, a new method for cell counting in immunocytochemistry images. It uses the Segment Anything Model (SAM) for efficient, accurate, zero-shot cell counting without manual labels.
Area of Science:
- Biomedical research
- Microscopy
- Computational biology
Background:
- Manual cell counting in immunocytochemistry is laborious and prone to errors.
- Deep learning methods require extensive, costly labeled datasets, hindering scalability.
- Accurate cell quantification is crucial for disease diagnosis and treatment research.
Purpose of the Study:
- To introduce IDCC-SAM, a novel application of the Segment Anything Model (SAM) for zero-shot cell counting in immunocytochemistry.
- To leverage SAM's pre-trained capabilities to eliminate the need for manual annotations.
- To enhance the scalability and efficiency of cell counting in microscopic imaging.
Main Methods:
- Utilized Meta AI's Segment Anything Model (SAM), pre-trained on a large image dataset.
- Adapted SAM for zero-shot cell counting specifically for fluorescent microscopic immunocytochemistry data.
- Evaluated IDCC-SAM on three public datasets: IDCIA, ADC, and VGG.
Main Results:
- IDCC-SAM achieved the lowest Mean Absolute Error on VGG (26) and ADC (28) datasets.
- Demonstrated the highest Acceptable Absolute Error across all tested datasets (28%, 26%, 33%).
- Outperformed state-of-the-art supervised (U-Net, Mask R-CNN) and zero-shot (NP-SAM, SAM4Organoid) models.
Conclusions:
- IDCC-SAM offers a scalable and efficient solution for cell counting in immunocytochemistry.
- The zero-shot approach significantly reduces the reliance on manual annotations and specialized models.
- This method has the potential to improve accuracy and accessibility in biomedical image analysis.
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.
