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Related Experiment Video

Updated: Jul 13, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Optimizing Deep Learning-Based Segmentation of Densely Packed Cells using Cell Surface Markers.

Sunwoo Han¹, Khamsone Phasouk¹, Jia Zhu¹

¹Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.

Research Square

|October 16, 2023

Summary

This summary is machine-generated.

Accurate cell segmentation in dense tissues is crucial for spatial profiling. Deep learning models, particularly Cellpose, significantly improved performance after targeted training and parameter tuning, outperforming initial benchmarks.

Keywords:

HSV cell segmentation computer vision deep learning

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Last Updated: Jul 13, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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Published on: February 15, 2022

Area of Science:

Computational biology
Image analysis
Immunofluorescence microscopy

Background:

Accurate cell segmentation is essential for spatial molecular profiling.
Identifying and quantifying cells in dense, inflamed tissues presents a significant challenge.

Conclusions:

Model selection and targeted training yield substantial improvements in cell segmentation performance.
The optimized model's performance is competitive with human evaluation.
Moderate signal-to-noise ratio in the dataset may limit ultimate performance.