Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Ogive Graph01:07

Ogive Graph

6.9K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.9K
Graphing Antiderivatives01:30

Graphing Antiderivatives

77
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
77
Graphs of Functions01:30

Graphs of Functions

361
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
361
Bar Graph01:07

Bar Graph

23.2K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.2K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Adaptive feature unlearning for trustworthy medical imaging privacy.

Medical image analysis·2026
Same author

Frequency disentanglement with State space gating network for medical image segmentation.

Medical & biological engineering & computing·2026
Same author

Dermal absorption and percutaneous penetration of p-phenylenediamines (PPDs) and p-phenylenediamine quinones (PPD-Qs): Mechanisms and implications for human dermal exposure risks.

Journal of hazardous materials·2026
Same author

Ultrasonic degradation of Dendrobium officinale polysaccharide: Kinetics, structural modification, and its impact on wheat starch digestibility.

Food research international (Ottawa, Ont.)·2026
Same author

Syndrome differentiation of Traditional Chinese Medicine via multiple knowledge enhancement with Kolmogorov-Arnold Theorem.

Artificial intelligence in medicine·2026
Same author

Parvimonas micra exacerbates periodontitis by infiltrating host cells through TmpC and circumventing lysosomal elimination via AppA.

EBioMedicine·2026

Related Experiment Video

Updated: Feb 14, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

25.0K

Graph-guided frequency-enhanced state space network for 3D spine segmentation from MR images.

Linghui Hong1,2,3, Zhengchao Zhou1,2,3, Wanbo Xu4,5

  • 1Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China.

Journal of Applied Clinical Medical Physics
|February 12, 2026
PubMed
Summary

A novel Graph-Guided Frequency-Enhanced State Space Network (GF-SSNet) achieves accurate 3D multi-modal spine MRI segmentation. This method improves global modeling and boundary delineation for better computer-aided spinal disease diagnosis.

Keywords:
frequency dynamic convolutiongraph convolutional networkspinal MRI segmentationstate space model

More Related Videos

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
07:54

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Published on: September 20, 2012

14.2K
Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF
08:34

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF

Published on: October 17, 2025

615

Related Experiment Videos

Last Updated: Feb 14, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

25.0K
In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
07:54

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Published on: September 20, 2012

14.2K
Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF
08:34

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF

Published on: October 17, 2025

615

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Accurate spinal MRI segmentation is crucial for diagnosing spinal diseases.
  • Existing methods struggle with complex anatomy and artifacts, limiting global modeling and boundary delineation.

Purpose of the Study:

  • To propose the Graph-Guided Frequency-Enhanced State Space Network (GF-SSNet) for accurate 3D multi-modal spine MRI segmentation.
  • To address limitations in global semantic modeling, cross-modal perception, and fine boundary identification.
  • To provide technical support for intelligent diagnosis and precision medicine in spinal diseases.

Main Methods:

  • The GF-SSNet utilizes a dual frequency-spatial enhancement mechanism with Frequency Dynamic Convolution (FDConv) and Three-Directional Mamba (TD-Mamba).
  • It incorporates Position-Aware Attention Fusion (PAAF) and Graph Convolutional Networks (GCN) for topological anatomical constraints.
  • A Depth-aware Progressive Upsampling (DAPU) strategy is used for fine-grained spatial information reconstruction.

Main Results:

  • GF-SSNet achieved superior performance on normal test sets, with Dice Mean of 92.04% and IoU Mean of 85.29%.
  • It significantly reduced HD95 to 3.06 mm and ASSD to 0.612 mm compared to baselines.
  • On pathological test sets, GF-SSNet maintained strong performance (Dice Mean 87.60%), demonstrating robustness despite segmentation challenges in degenerative conditions.

Conclusions:

  • GF-SSNet effectively segments spinal MRIs by fusing frequency features and global dependencies.
  • The method offers improved technical support for intelligent diagnosis of spinal diseases.
  • Ablation studies and loss function analysis validated the contribution of each component.