Related Experiment Video
Updated: Aug 9, 2026
Using Computer Vision Libraries to Streamline Nuclei Quantification
Published on: June 6, 2025
[Morphologic quantitative analysis of nucleus in laryngeal squamous cell carcinoma with Automatic Image Cytometry]
Tao Wang1, Hongyan Huang, Yonghua Cui
1Department of Otolaryngology, 9th Hospital, Medical College, Shanghai Jiaotong University, China. wang_tao_2001@yahoo.com.cn
Objective:
Analyze morphological character of nucleus in laryngeal squamous cell carcinoma with Automatic Image Cytometry, discuss the relation between morphological parameters and tumor clinic.
Method:
We discussed morphological character of nucleus from 83 cytology specimens which included 21 laryngeal squamous cell carcinomas and 20 benign diseases with Automatic Image Cytometry.
Result:
Nucleus from laryngeal squamous cell carcinomas had bigger nuclear area nuclear radius, nuclear perimeter and more DNA content per nuclear area than from laryngeal benign diseases, furthermore, these parameters showed distinct difference in different anatomic positions and different pathologic types. We concluded that nuclear areas of nucleus in S phase from laryngeal squamous cell carcinomas are more than 77 microm, Second Order Regression Equation(Y =aX2+bX+c) described scattered diagrams between mean nuclear radius and nuclear area of laryngeal cytology specimens with different larynx pathology and different cell phase in cell cycle. The more the value of coefficient "c"(c>1.82) and the less the value of coefficient "b" (b< 0.045), the more the probability of malignant tumor diagnosis.
Conclusion:
Morphologic quantitative analysis parameters, such as nuclear area, mean nuclear radius, nuclear perimeter and DNA content per nuclear area, are suitable for diagnosing laryngeal squamous cell carcinomas. They could offer valuable morphologic quantitative criterion in assisting cytology diagnose and evaluating tumor clinical characters when these parameters associated with nuclear area of nucleus in S-phase and the coefficients of Second Order Regression Equation.
