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

Reducing Line Loss01:18

Reducing Line Loss

524
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
524
Deconvolution01:20

Deconvolution

764
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
764
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

920
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
920
Region of Convergence01:17

Region of Convergence

1.1K
The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
1.1K
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

482
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
482
Visual Agnosia01:12

Visual Agnosia

2.0K
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
2.0K

You might also read

Related Articles

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

Sort by
Same author

An image quality assessment algorithm based on 'global + local' feature fusion.

PeerJ. Computer science·2025
Same author

A quality assessment algorithm for no-reference images based on transfer learning.

PeerJ. Computer science·2025
Same author

A destructive active defense algorithm for deepfake face images.

PeerJ. Computer science·2024
Same author

Multi-task learning with multi-gate mixture of transformer-based experts for predictive process monitoring in manufacturing.

Science progress·2024
Same author

ChineseMPD: A Semantic Segmentation Dataset of Chinese Martial Arts Classic Movie Props.

Scientific data·2024
Same author

Cascade spatial and channel-wise multifusion network with criss cross augmentation for corneal segmentation and reconstruction.

Computers in biology and medicine·2024
Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Anatomically and biochemically guided deep image prior for sodium MRI denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Segment Anything Model for medical image segmentation: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

HiCAF-Net: A Hierarchical Cross-Attention Fusion framework for cross-cancer subtype classification using histopathological and genomic data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
See all related articles

Related Experiment Video

Updated: May 2, 2026

Anterior Segment Organ Culture Platform for Tracking Open Globe Injuries and Therapeutic Performance
07:27

Anterior Segment Organ Culture Platform for Tracking Open Globe Injuries and Therapeutic Performance

Published on: August 25, 2021

2.0K

Encoder-shared visual state space network for anterior segment reconstruction.

Guiping Qian1, Huaqiong Wang1, Shan Luo1

  • 1School of Media Engineering, Communication University of Zhejiang, Hangzhou, 310018, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|August 21, 2025
PubMed
Summary
This summary is machine-generated.

A novel network unifies image alignment and segmentation for 3D anterior segment reconstruction from AS-OCT scans, improving accuracy in cornea and iris analysis.

Keywords:
AS-OCT imageAnterior segment reconstructionHomography estimationImage alignmentState space model

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

491

Related Experiment Videos

Last Updated: May 2, 2026

Anterior Segment Organ Culture Platform for Tracking Open Globe Injuries and Therapeutic Performance
07:27

Anterior Segment Organ Culture Platform for Tracking Open Globe Injuries and Therapeutic Performance

Published on: August 25, 2021

2.0K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

491

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • 3D anterior segment reconstruction from AS-OCT is crucial for diagnosing eye conditions like keratitis.
  • Current methods struggle with image alignment and accurate corneal segmentation.

Purpose of the Study:

  • To develop a unified framework for 3D anterior segment reconstruction addressing image alignment and segmentation challenges.
  • To enhance the accuracy of corneal segmentation and 3D visualization.

Main Methods:

  • Proposed an encoder-shared visual state space network integrating image alignment and segmentation.
  • Utilized visual state space projection for image alignment and channel-wise fusion for segmentation.
  • Employed a decoder block to capture contextual dependencies and improve feature representation.

Main Results:

  • Achieved remarkable performance in anterior segment alignment, corneal segmentation, and 3D reconstruction on AIDK-Align and CORNEA datasets.
  • Demonstrated superior alignment and segmentation precision compared to state-of-the-art methods.
  • Successfully reconstructed accurate 3D volume data from aligned and segmented images.

Conclusions:

  • The proposed encoder-shared visual state space network effectively tackles challenges in 3D anterior segment reconstruction.
  • This unified approach significantly improves diagnostic capabilities for anterior segment eye diseases.
  • The method offers enhanced precision for both image alignment and corneal segmentation.