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

Survival Tree01:19

Survival Tree

464
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
464
Phylogenetic Trees03:21

Phylogenetic Trees

51.7K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
51.7K
Structural Classification of Joints01:20

Structural Classification of Joints

8.5K
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...
8.5K

You might also read

Related Articles

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

Sort by
Same author

A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.

Nature communications·2026
Same author

Incorporating modality-specific intensity prior as text prompt for multimodal myocardial pathology segmentation.

Medical image analysis·2026
Same author

StrucGAP: a modular, streamlined and traceable data mining platform for structural and site-specific glycoproteomics.

Nature communications·2026
Same author

Rejoining fragmented ancient bamboo slips with physics-driven deep learning.

Nature communications·2026
Same author

Development and validation evaluation of the depressive disorders self-management scale for adolescents.

BMC psychology·2026
Same author

A comprehensive N-glycoproteome atlas reveals tissue-specific glycan remodeling but non-random structural microheterogeneities.

Nature communications·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Mar 22, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.7K

Hierarchical Segmentation Using Tree-Based Shape Spaces.

Yongchao Xu, Edwin Carlinet, Thierry Geraud

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 22, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel image segmentation method using shape space and extinction values to create a new saliency map. This approach enhances image partitioning by offering more segmentation possibilities beyond traditional hierarchy cuts.

    More Related Videos

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    264
    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

    25.1K

    Related Experiment Videos

    Last Updated: Mar 22, 2026

    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.7K
    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    264
    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

    25.1K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Geometry

    Background:

    • Current image segmentation trends focus on creating hierarchies from fine to coarse segmentations.
    • Traditional methods use scale-set theory to find optimal cuts in these hierarchies.
    • Existing methods are limited by the fixed structure of the hierarchy, restricting segmentation outcomes.

    Purpose of the Study:

    • To propose a novel approach for image segmentation by transforming existing hierarchies.
    • To develop a new saliency map based on shape space and extinction values.
    • To expand the range of possible image partitions obtainable from a given hierarchy.

    Main Methods:

    • Constructing a shape space, a graph representation of image regions with attributes.
    • Identifying meaningful regions (local minima) within the shape space.
    • Weighing region boundaries using extinction values based on region attributes to create a saliency map.

    Main Results:

    • The proposed method generates an extinction-based saliency map, representing a new hierarchy of segmentations.
    • Each threshold of the saliency map yields segmentations distinct from traditional hierarchy cuts.
    • The approach effectively highlights regions with specific characteristics.

    Conclusions:

    • The novel method significantly enlarges the set of possible image partitions from a hierarchy.
    • The approach offers greater flexibility and richer segmentation results compared to classical methods.
    • Illustrations confirm the method's usefulness in both qualitative and quantitative evaluations.