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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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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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Shape and Texture of Coarse Aggregate01:25

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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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Functional Classification of Joints01:09

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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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Introduction to Joints00:58

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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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Uniform Depth Channel Flow01:27

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition.

Xijing Wang1, Hongcheng Han1, Mengrui Xu1,2

  • 1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.

Frontiers in Neuroscience
|July 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stream network to improve medical image analysis by enhancing shape feature representation. The method effectively distinguishes between shape and texture, outperforming existing algorithms in melanoma recognition tasks.

Keywords:
brain-like information processingcomputer-aided diagnosisfeature fusionimage recognitionjoint learningtwo-stream network

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Area of Science:

  • Computer Vision
  • Medical Image Analysis
  • Artificial Intelligence

Background:

  • Medical image analysis often struggles with shape feature representation due to pre-training biases.
  • Current methods using datasets like ImageNet improve texture but neglect crucial shape information.
  • Accurate shape analysis is vital for many medical imaging diagnostic tasks.

Purpose of the Study:

  • To enhance shape feature representation in medical image analysis using a novel two-stream network.
  • To improve the accuracy and robustness of computer-aided diagnosis systems.
  • To address the limitations of existing pre-trained models in capturing shape characteristics.

Main Methods:

  • Proposed a shape-and-texture-biased two-stream network with multi-task joint learning.
  • Utilized pyramid-grouped convolution for texture enhancement and deformable convolution for shape feature extraction.
  • Implemented a channel-attention-based feature selection module for effective feature fusion and an asymmetric loss function for sample imbalance.

Main Results:

  • The proposed method demonstrated superior performance in melanoma recognition tasks on ISIC-2019 and XJTU-MM datasets.
  • Experimental results on dermoscopic and pathological image recognition validated the method's effectiveness.
  • The network successfully enhanced shape feature representation compared to existing algorithms.

Conclusions:

  • The shape-and-texture-biased two-stream network significantly improves medical image analysis by prioritizing shape features.
  • The method offers a robust solution for tasks requiring detailed shape analysis, such as lesion recognition.
  • This approach advances knowledge-guided medical image analysis and computer-aided diagnosis.