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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: Jan 5, 2026

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

Joint optic disc and cup segmentation using semi-supervised conditional GANs.

Shaopeng Liu¹, Jiaming Hong², Xu Lu³

¹Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510006, China; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China.

Computers in Biology and Medicine

|October 21, 2019

Summary

This summary is machine-generated.

Accurate segmentation of the optic disc and cup is crucial for glaucoma diagnosis. This study introduces a semi-supervised Generative Adversarial Network (GAN) method that improves segmentation performance using limited labeled data.

Keywords:

Deep learning General adversarial nets Glaucoma screening Medical image Semi-supervised learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma is a leading cause of irreversible vision loss.
Accurate segmentation of the optic disc and cup in fundus images is vital for glaucoma screening.
Existing segmentation methods struggle due to a lack of pixel-level annotated data.

Purpose of the Study:

To develop an effective joint optic disc and cup segmentation method.
To address the challenge of limited annotated data in medical image segmentation.
To improve glaucoma diagnosis through enhanced fundus image analysis.

Main Methods:

Proposed a semi-supervised conditional Generative Adversarial Network (GAN) architecture.
The architecture includes a segmentation network, generator, and discriminator.
Utilized both labeled and unlabeled fundus image data for training.

Main Results:

Achieved state-of-the-art performance in optic disc and cup segmentation.
Demonstrated superior segmentation accuracy on the ORIGA and REFUGE datasets.
The semi-supervised approach effectively leveraged unlabeled data to boost performance.

Conclusions:

The proposed semi-supervised GAN method offers a robust solution for optic disc and cup segmentation.
This approach significantly enhances segmentation accuracy, particularly when labeled data is scarce.
The findings contribute to improved glaucoma screening and diagnosis through advanced AI in medical imaging.