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Related Concept Videos

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
Bone Cells and Tissue01:30

Bone Cells and Tissue

Bones contain a relatively small number of cells entrenched in a matrix of organic and inorganic components. Although bone cells compose only a small amount of the bone volume, they are crucial to its function. Four types of cells are found within the bone tissue— osteoblasts, osteocytes, osteogenic cells, and osteoclasts.
Osteoblasts and Osteocytes
The osteoblast is the bone cell responsible for forming new bone tissue. It is found in the growing portions of bone, including the periosteum and...
Bone Remodeling and Repair01:31

Bone Remodeling and Repair

Osteoclasts are cells responsible for bone resorption and remodeling. They originate from hematopoietic progenitor cells present in the bone marrow. Numerous progenitor cells fuse to form multinucleated cells, each with 10-20 nuclei. A single osteoclast has a diameter of 150 to 200 µM. These cells have ruffled borders that break down the underlying bone tissue and release minerals such as calcium into the blood in bone resorption. Osteoclasts cling to bones with their ruffled edges during bone...
Bone Remodeling01:40

Bone Remodeling

Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.

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Related Experiment Video

Updated: Jun 12, 2026

Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma
08:07

Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma

Published on: April 12, 2019

ECM-Aware Cell-Graph Mining for Bone Tissue Modeling and Classification.

Cemal Cagatay Bilgin1, Peter Bullough, George E Plopper

  • 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Data Mining and Knowledge Discovery
|June 15, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces ECM-aware cell-graph mining for automated bone cancer diagnosis from histopathology images. This method improves accuracy by analyzing cell distribution and extracellular matrix composition, achieving 90.0% accuracy.

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Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

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

Published on: April 8, 2015

Area of Science:

  • Computational pathology
  • Bioinformatics
  • Medical image analysis

Background:

  • Pathological examination of bone biopsies is standard for cancer diagnosis but suffers from observer subjectivity.
  • Automated tissue modeling and classification can enhance diagnostic accuracy and reduce subjectivity.

Purpose of the Study:

  • To present a novel graph theoretical method, ECM-aware cell-graph mining, for accurate bone cancer diagnosis.
  • To model structure-function relationships in bone tissues by integrating extracellular matrix (ECM) formation and cell distribution.

Main Methods:

  • Histopathological images are segmented using Hessian matrix eigenvalues to identify cell nuclei as graph nodes.
  • Nodes are color-coded based on surrounding ECM composition, and edges connect adjacent cells of the same color.
  • Topological and spectral features of these colored cell-graphs are computed for classification.

Main Results:

  • The ECM-aware cell-graph approach achieved 90.0% classification accuracy for bone tissue samples (healthy, fractured, cancerous).
  • This significantly outperforms Delaunay triangulation (75.0%) and simple cell-graph methods (81.1%).

Conclusions:

  • ECM-aware cell-graph mining offers a precise method for classifying bone tissue functional states.
  • This approach holds promise for reducing subjectivity and improving accuracy in automated bone cancer diagnosis.