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Related Concept Videos

Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
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Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
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Joints01:26

Joints

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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
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Trial and Error and Algorithm01:12

Trial and Error and Algorithm

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Introduction to Joints00:58

Introduction to Joints

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The adult human body usually has 206 bones, and except for the hyoid bone in the neck, each bone is connected to at least one other bone. Joints are the location where bones come together. Many joints allow for movement between the bones. At these joints, the articulating surfaces of the adjacent bones can move smoothly against each other. However, the bones of other joints may be joined by connective tissue or cartilage. These joints are designed for stability and provide little or no...
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A Partially-Learned Algorithm for Joint Photo-acoustic Reconstruction and Segmentation.

Yoeri E Boink, Srirang Manohar, Christoph Brune

    IEEE Transactions on Medical Imaging
    |June 11, 2019
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    Summary

    This study introduces a novel method to simultaneously reconstruct and segment photoacoustic images, improving vascular geometry visualization. The developed algorithm is robust and computationally efficient, outperforming existing techniques.

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    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Computational Science

    Background:

    • Photoacoustic imaging (PAI) reconstruction often struggles with inhomogeneous illumination, obscuring details like vascular geometry.
    • Image segmentation is typically a separate post-processing step, adding complexity and potential for error.

    Purpose of the Study:

    • To develop a method for joint photoacoustic image reconstruction and segmentation.
    • To create a robust algorithm stable against variations in initial pressures and system settings.
    • To validate the approach on challenging photoacoustic tomography data.

    Main Methods:

    • Modification of a partially learned algorithm based on a convolutional neural network (CNN).
    • Investigation of algorithm stability against input and system parameter variations.
    • Validation using synthetic and experimental photoacoustic tomography data in limited view/angle scenarios.

    Main Results:

    • The proposed method achieves higher quality reconstructions and segmentations compared to state-of-the-art methods.
    • The algorithm demonstrates robustness to input and system settings.
    • It is computationally less expensive than traditional iterative reconstruction methods.

    Conclusions:

    • Joint reconstruction and segmentation in photoacoustic imaging is feasible and effective using a modified CNN approach.
    • The developed method offers improved accuracy and efficiency for extracting vascular information.
    • The approach is versatile and adaptable to other imaging modalities and tasks.