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Related Concept Videos

Classification of Bones01:18

Classification of Bones

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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.
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Structural Classification of Joints01:20

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Bone Remodeling01:40

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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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Bone Structure01:55

Bone Structure

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Within the skeletal system, the structure of a bone, or osseous tissue, can be exemplified in a long bone, like the femur, where there are two types of osseous tissue: cortical and cancellous.
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Gross Anatomy of Bone01:17

Gross Anatomy of Bone

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The two main features of a long bone are the diaphysis and the epiphysis.
The diaphysis is the tubular shaft that runs between the proximal and distal ends of the bone. The walls of the diaphysis are composed of dense and hard compact bone made of numerous osteons — the functional unit of the compact bone. The hollow region in the diaphysis is called the medullary cavity, which harbors the bone marrow. In infants and children, this marrow cavity is filled with red marrow, whereas in...
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Updated: Jan 8, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
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Automated Joint Space Detection Improves Bone Segmentation Accuracy.

H Mark Kenney1, Daniel Lichau2, Rémi Blanc2

  • 1Center for Musculoskeletal Research, University of Rochester Medical Center; Department of Pathology & Laboratory Medicine, University of Rochester Medical Center; Department of Medicine, University of Rochester Medical Center; howard_kenney@urmc.rochester.edu.

Journal of Visualized Experiments : Jove
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PubMed
Summary
This summary is machine-generated.

Deep learning models automate the analysis of mouse paw anatomy from micro-CT scans, significantly improving joint space segmentation accuracy compared to previous methods. Performance decreases with increased arthritis severity and age.

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Area of Science:

  • Biomedical Imaging
  • Computational Anatomy
  • Deep Learning Applications

Background:

  • Manual segmentation of complex anatomical structures is time-consuming, requires expertise, and suffers from interobserver variability.
  • Digital image analysis, particularly deep learning (DL), offers automated solutions for landmark detection and segmentation.
  • Previous semi-automated (SA) methods have limitations in accuracy and efficiency for detailed anatomical analysis.

Purpose of the Study:

  • To develop and evaluate a supervised deep learning model for automated segmentation of joint spaces in murine paws.
  • To compare the performance of the DL model against existing SA approaches.
  • To assess the impact of disease severity and age on segmentation accuracy in a mouse model of inflammatory arthritis.

Main Methods:

  • Supervised automated analysis of micro-computed tomography (micro-CT) datasets of murine hindpaws and forepaws.
  • Utilized a 3D U-Net DL architecture with a ResNet-18 backbone for joint space prediction.
  • Incorporated structure enhancement, tensor voting, and output dilation to refine segmentation.
  • Trained the model using wild-type (WT) hindpaw labels as ground truth and tested on WT and TNF-Tg mice.

Main Results:

  • The DL joint space prediction model significantly improved segmentation accuracy compared to SA methods in healthy joints.
  • Segmentation accuracy decreased with increasing disease severity and age in TNF-Tg mice.
  • Testing on forepaws also showed reduced accuracy with higher arthritic severity.

Conclusions:

  • The developed supervised DL model outperforms recent SA approaches for segmenting healthy joint spaces in murine paws.
  • While the model's performance degrades with disease progression and age, it demonstrates potential for catalyzing further development in structure-specific segmentation.
  • This automated approach can enhance the investigation of complex bone anatomy and disease-modified structures.