CellFMCount: A Fluorescence Microscopy Dataset, Benchmark, and Methods for Cell Counting

• URL: http://arxiv.org/abs/2511.19351v1
• Date: Mon, 24 Nov 2025 17:53:59 GMT
• Title: CellFMCount: A Fluorescence Microscopy Dataset, Benchmark, and Methods for Cell Counting
• Authors: Abdurahman Ali Mohammed, Catherine Fonder, Ying Wei, Wallapak Tavanapong, Donald S Sakaguchi, Qi Li, Surya K. Mallapragada,
• Abstract summary: We introduce a large-scale annotated dataset comprising $3,023$ images from immunocytochemistry experiments related to cellular differentiation.<n>The dataset presents significant challenges: high cell density, overlapping and morphologically diverse cells, a long-tailed distribution of cell count per image, and variation in staining protocols.<n>We implement a density-map-based adaptation of the Segment Anything Model (SAM) and report a mean absolute error (MAE) of $22.12$, which outperforms existing approaches.
• Score: 10.191134773938566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate cell counting is essential in various biomedical research and clinical applications, including cancer diagnosis, stem cell research, and immunology. Manual counting is labor-intensive and error-prone, motivating automation through deep learning techniques. However, training reliable deep learning models requires large amounts of high-quality annotated data, which is difficult and time-consuming to produce manually. Consequently, existing cell-counting datasets are often limited, frequently containing fewer than $500$ images. In this work, we introduce a large-scale annotated dataset comprising $3{,}023$ images from immunocytochemistry experiments related to cellular differentiation, containing over $430{,}000$ manually annotated cell locations. The dataset presents significant challenges: high cell density, overlapping and morphologically diverse cells, a long-tailed distribution of cell count per image, and variation in staining protocols. We benchmark three categories of existing methods: regression-based, crowd-counting, and cell-counting techniques on a test set with cell counts ranging from $10$ to $2{,}126$ cells per image. We also evaluate how the Segment Anything Model (SAM) can be adapted for microscopy cell counting using only dot-annotated datasets. As a case study, we implement a density-map-based adaptation of SAM (SAM-Counter) and report a mean absolute error (MAE) of $22.12$, which outperforms existing approaches (second-best MAE of $27.46$). Our results underscore the value of the dataset and the benchmarking framework for driving progress in automated cell counting and provide a robust foundation for future research and development.

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