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

Explicit Memories01:27

Explicit Memories

470
Explicit memories, also known as declarative memories, are consciously remembered, recalled, and reported. Studying for a chemistry exam involves material that will become part of explicit memory. There are two types of explicit memory: episodic and semantic.
Episodic memory contains information about personally experienced events and is reported as a story. An example of episodic memory is recalling a birthday celebration. This type of memory includes the what, where, and when of an event, as...
470
Implicit Differentiation01:25

Implicit Differentiation

72
In classical mechanics, motion is often described through relationships between spatial coordinates and time. A car moving along a straight highway with constant acceleration serves as a simple case where velocity is an explicit function of time. This scenario results in a linear equation, enabling straightforward analysis using basic differentiation techniques.In contrast, a satellite in circular orbit follows a path defined by an implicit function. The position of the satellite is constrained...
72
Implicit Memories01:24

Implicit Memories

479
Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
479
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
Implicit Differentiation: Problem Solving01:29

Implicit Differentiation: Problem Solving

65
Curves defined implicitly, where variables cannot be separated algebraically, require specialized techniques for analysis. The conchoid of Nicomedes exemplifies such a case. Its equation links x and y in a way that prevents isolation of one variable, making implicit differentiation essential to determine the slope and behavior at any point on the curve.The implicit form of the conchoid can be expressed as:To differentiate this equation, y is treated as a function of x, and the chain rule is...
65
Second Derivatives of Implicit Functions01:29

Second Derivatives of Implicit Functions

88
Elliptical arches are fundamental in architectural and structural engineering, offering aesthetic appeal and structural efficiency. The shape of an elliptical arch follows a constrained geometric relationship where the height and horizontal position are implicitly related. This means that the height y cannot be explicitly expressed as a function of the horizontal position x, necessitating implicit differentiation for slope and curvature analysis.The equation of an ellipse centered at the origin...
88

You might also read

Related Articles

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

Sort by
Same author

Hierarchical multi-level dynamic hyperparameter deformable image registration with convolutional neural network.

Physics in medicine and biology·2024
Same author

Association of Smoking with Metabolic Volatile Organic Compounds in Exhaled Breath.

International journal of molecular sciences·2017
Same author

Self-assembly of peptide amphiphiles for drug delivery: the role of peptide primary and secondary structures.

Biomaterials science·2017
Same author

NARRMDA: negative-aware and rating-based recommendation algorithm for miRNA-disease association prediction.

Molecular bioSystems·2017
Same author

Ultrasensitive, high-dynamic-range and broadband strain sensing by time-of-flight detection with femtosecond-laser frequency combs.

Scientific reports·2017
Same author

MicroRNAs and complex diseases: from experimental results to computational models.

Briefings in bioinformatics·2017

Related Experiment Video

Updated: Feb 15, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

MS-HIENet: multi-scale hybrid implicit-explicit registration network.

Zhijia Wang1, Xinyu Liu1, Xing Chen1

  • 1School of Control Science and Engineering, Shandong University, Jinan, Shandong Province, People's Republic of China.

Physics in Medicine and Biology
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning framework for lung image registration, improving accuracy in diagnosing and treating dynamic organs. The new method enhances precision by effectively handling both large and small deformations.

Keywords:
deformable registrationimplicit neural representationlung CTmulti-scale

More Related Videos

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

9.1K
Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay
05:16

Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay

Published on: July 26, 2021

2.0K

Related Experiment Videos

Last Updated: Feb 15, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

9.1K
Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay
05:16

Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay

Published on: July 26, 2021

2.0K

Area of Science:

  • Medical imaging
  • Computational anatomy
  • Deep learning for medical applications

Background:

  • Medical image registration is crucial for precision diagnosis and treatment, especially for deformable organs like lungs.
  • Existing deep learning methods struggle to balance large deformation modeling with fine structure preservation due to limited receptive fields.
  • Pulmonary imaging presents challenges due to high deformability and complex motion patterns.

Purpose of the Study:

  • To develop a novel deep learning framework for jointly handling large-scale and localized deformations in pulmonary imaging.
  • To address the limitations of existing methods in optimizing deformation modeling and preserving fine structures.
  • To improve the accuracy and efficiency of medical image registration for dynamic organs.

Main Methods:

  • Proposed a Multi-Scale Hybrid Implicit-Explicit Registration Network (MS-HIENet), a mask-free, end-to-end framework integrating Implicit Neural Representation (INR) and Convolutional Neural Networks (CNN).
  • Employed a multi-scale optimization strategy: low-resolution INR for global deformations and high-resolution CNN for local refinement (coarse-to-fine registration).
  • Utilized an INR-based coordinate-to-displacement implicit mapping to directly model continuous deformation fields without mask annotations.

Main Results:

  • MS-HIENet achieved a mean Target Registration Error (TRE) of 1.00 mm on the DIR-Lab dataset.
  • Demonstrated an average reduction of 29.5% in TRE compared to state-of-the-art deep learning methods.
  • Ablation studies confirmed the effectiveness of multi-scale collaboration and hybrid implicit-explicit representation, with minimal deformation field folding (mean: 0.00017).

Conclusions:

  • The MS-HIENet effectively bridges the gap between global deformation consistency and local anatomical precision in lung image registration.
  • Combining INR's continuous modeling with CNN's local feature refinement enhances topological consistency and clinical applicability.
  • Offers a robust solution for high-precision lung image analysis, improving diagnosis and treatment planning.