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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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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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...
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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Related Experiment Video

Updated: Feb 3, 2026

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo
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Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

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A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using

Arunava Chakravarty1, Jayanthi Sivaswamy1

  • 1Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad 500032, India.

Computer Methods and Programs in Biomedicine
|October 20, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for segmenting retinal layers in OCT images, improving accuracy for diagnosing eye diseases like AMD and DME without manual feature engineering. The approach adapts to pathologies by retraining on relevant datasets.

Keywords:
Age-Related macular degenerationConditional random fieldDiabetic macular edemaOptical coherence tomographyStructured support vector machines

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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate segmentation of intra-retinal tissue layers in Optical Coherence Tomography (OCT) images is crucial for diagnosing and treating ocular diseases like Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME).
  • Existing energy minimization methods often require manual feature engineering and struggle with pathological cases.

Purpose of the Study:

  • To develop an end-to-end trainable method for joint multi-layer segmentation of OCT B-scans that learns cost functions from data.
  • To create a system adaptable to various pathologies through retraining.

Main Methods:

  • A Conditional Random Field (CRF) framework is proposed for joint multi-layer segmentation of OCT B-scans.
  • Retinal layer and boundary appearance are modeled using convolutional filter banks, and shape priors use Gaussian distributions.
  • A Structured Support Vector Machine formulation enables joint, end-to-end training by linearly parameterizing the total CRF energy.

Main Results:

  • The proposed method outperformed three benchmark algorithms on four public datasets (NORMAL-1, NORMAL-2, AMD-1, DME-1).
  • Achieved an average unsigned boundary localization error (U-BLE) as low as 1.11 pixels and Dice coefficients up to 0.98 across different datasets and layers.
  • Demonstrated superior performance in segmenting healthy and pathological retinal layers.

Conclusions:

  • A supervised CRF-based method for joint multi-layer segmentation of OCT images has been developed.
  • This method can assist ophthalmologists in quantitative analysis of retinal structural changes for clinical practice and research.
  • The approach offers improved diagnostic capabilities for ocular diseases.