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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
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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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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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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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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
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Unsupervised classification of tissues composition for Monte Carlo dose calculation.

Arthur Lalonde1, Charlotte Remy1, Mikaël Simard1

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K-means clustering effectively reduces materials for Monte Carlo (MC) dose calculations. Using Chi-Squared distance minimizes errors in dose distribution, proving beneficial for low-kV photons and proton therapy.

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Area of Science:

  • Medical Physics
  • Computational Biology
  • Data Science

Background:

  • Monte Carlo (MC) simulations are crucial for accurate dose calculations in radiation therapy.
  • Reducing the number of materials in MC simulations can improve computational efficiency.
  • K-means clustering offers a potential method for material simplification.

Purpose of the Study:

  • To investigate k-means clustering for reducing material variety in MC dose calculations.
  • To evaluate the impact of different distance measures on dose distribution accuracy.
  • To compare clustering performance against supervised classification methods.

Main Methods:

  • A numerical phantom with 31 human tissues was created.
  • K-means clustering with Euclidean, Standardized Euclidean, Chi-Squared, and Cityblock distances was applied.
  • MC simulations calculated dose distributions for low-kV photons and MeV protons.
  • Energy absorption coefficients (EAC) and proton stopping powers (SPR) were analyzed.

Main Results:

  • Chi-Squared distance yielded the smallest error in dose distribution.
  • Significant differences in EAC and SPR were observed between distance metrics.
  • K-means clustering demonstrated performance comparable to supervised classification for proton therapy.
  • The method showed particular benefit for low-kV photon applications.

Conclusions:

  • K-means clustering is a viable method to reduce material complexity in MC dose calculations.
  • The Chi-Squared distance measure is recommended for optimal accuracy.
  • This approach offers efficiency gains without compromising, and sometimes improving, accuracy.