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Functional Classification of Joints

Functional Classification of Joints

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
An...

Structural Classification of Joints

Structural Classification of Joints

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.
A fibrous joint is where the adjacent bones are united by fibrous connective...

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Related Experiment Video

Updated: Dec 30, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation.

Balamurali Murugesan, Kaushik Sarveswaran, Sharath M Shankaranarayana

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 18, 2020

Summary

This study introduces Psi-Net, a novel deep learning architecture for medical image segmentation. Psi-Net uses parallel decoders to improve segmentation smoothness and accuracy by predicting contours and distance maps alongside masks.

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Area of Science:

Medical Imaging
Computer Vision
Deep Learning

Background:

U-Net based networks are widely used for medical image segmentation.
Existing U-Net models often produce coarse segmentations with discontinuities.

Purpose of the Study:

To improve the smoothness and accuracy of medical image segmentation.
To refine the performance of U-Net like networks.

Main Methods:

Proposed a novel architecture named Psi-Net with a single encoder and three parallel decoders.
Psi-Net performs joint training for segmentation mask prediction, contour detection, and distance map estimation.
Introduced a new joint loss function combining Negative Log Likelihood and Mean Square Error.

Main Results:

Psi-Net demonstrated improved segmentation, boundary, and shape metrics in experiments.
Auxiliary tasks of contour and distance map prediction aided in capturing shape and boundary information.
Evaluated on optic cup/disc and polyp segmentation datasets.

Conclusions:

Psi-Net effectively refines medical image segmentation by addressing discontinuities.
The parallel decoder approach with auxiliary tasks enhances segmentation quality.
The proposed architecture shows promise for various medical image segmentation applications.