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Structural Classification of Joints01:20

Structural Classification of Joints

6.6K
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...
6.6K
Functional Classification of Joints01:09

Functional Classification of Joints

6.2K
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...
6.2K
Dense Connective Tissue01:13

Dense Connective Tissue

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Dense connective tissue contains more collagen fibers than loose connective tissue. As a consequence, it displays greater resistance to stretching. There are two major categories of dense connective tissue— regular and irregular.
Dense Regular Connective Tissue
In dense regular connective tissue, fibers are arranged parallel to each other, enhancing its tensile strength and resistance to stretching in the direction of the fiber orientations. Ligaments and tendons are made of dense regular...
11.4K
Computed Tomography01:10

Computed Tomography

7.8K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.8K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

188
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
188
Modeling and Similitude01:12

Modeling and Similitude

513
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
513

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Related Experiment Video

Updated: Dec 17, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval.

Wei-Zhi Nie, An-An Liu, Sicheng Zhao

    IEEE Transactions on Cybernetics
    |July 1, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Deep Correlated Joint Network (DCJN) for 2-D image-based 3-D model retrieval. The novel approach effectively extracts cross-modality features, improving similarity matching and outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • 3-D model retrieval from 2-D images is challenging due to modality differences.
    • Existing methods often struggle with effective cross-modality feature representation.

    Purpose of the Study:

    • To propose a novel Deep Correlated Joint Network (DCJN) for enhanced 2-D image-based 3-D model retrieval.
    • To develop a global loss function that jointly learns features and mitigates distribution discrepancies across modalities.

    Main Methods:

    • The DCJN jointly learns two distinct deep neural networks for feature extraction.
    • A global loss function, comprising discriminative and correlation losses, is proposed.
    • Discriminative loss minimizes intra-class distance and maximizes inter-class distance.
    • Correlation loss addresses distribution discrepancies between 2-D and 3-D modalities.

    Main Results:

    • The method achieves superior performance in 2-D image-based 3-D model retrieval.
    • Experimental results on a newly contributed large-scale dataset and popular benchmarks validate the approach.
    • The DCJN demonstrates significant improvements over state-of-the-art methods.

    Conclusions:

    • The proposed DCJN effectively extracts cross-modality features for 3-D model retrieval.
    • The novel global loss function is key to mitigating distribution discrepancies and improving retrieval accuracy.
    • This work advances the field of image-based 3-D model retrieval.