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Related Concept Videos

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The glandular epithelium is made of one or more epithelial cells modified to synthesize and secrete chemical substances. Glandular epithelia can be classified based on cell number. Unicellular glands have individual secretory cells scattered across the epithelial monolayer. In contrast, multicellular glands consist of a hollow tubular duct attached to the cluster of secretory cells located in the deep pockets.
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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
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Related Experiment Video

Updated: Apr 7, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
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Computational hepatocellular carcinoma tumor grading based on cell nuclei classification.

Chamidu Atupelage1, Hiroshi Nagahashi1, Fumikazu Kimura2

  • 1Tokyo Institute of Technology , Imaging Science and Engineering Laboratory, 4259-R2-51, Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan.

Journal of Medical Imaging (Bellingham, Wash.)
|July 10, 2015
PubMed
Summary

This study developed an automated method to grade hepatocellular carcinoma (HCC) by analyzing liver cell nuclei features. The system achieved 95.97% accuracy, highlighting nuclear texture as a key diagnostic indicator.

Keywords:
cancer gradingclassificationhepatocellular carcinoma histological imagesmultifractal computationmultifractal measuressegmentationtextural feature descriptor

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computational Pathology

Background:

  • Hepatocellular carcinoma (HCC) is the most common primary liver cancer.
  • Accurate grading of HCC malignancy is crucial for patient prognosis.
  • Computer-aided diagnosis (CAD) systems can automate HCC grading using image analysis.

Purpose of the Study:

  • To propose an automated method for HCC grading utilizing only liver cell nuclei.
  • To exclude non-liver cell nuclei to improve diagnostic accuracy.
  • To evaluate the significance of excluding non-liver cell nuclei in HCC grading.

Main Methods:

  • Developed a pipeline to exclude non-liver cell nuclei in two modules.
  • Extracted four categories of liver cell nuclear features for classification.
  • Utilized machine learning for HCC tumor classification based on nuclear features.

Main Results:

  • Nuclear texture was identified as the dominant feature for HCC grading.
  • Other nuclear features contributed to enhanced classification accuracy.
  • The proposed method achieved a 95.97% correct classification rate on HCC regions of interest.

Conclusions:

  • Automated exclusion of non-liver cell nuclei is significant for accurate HCC grading.
  • Liver cell nuclear features, particularly texture, are effective for HCC classification.
  • The developed CAD system demonstrates high accuracy for automated HCC diagnosis.