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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

404
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
404
Convolution Properties II01:17

Convolution Properties II

585
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
585
Network Covalent Solids02:18

Network Covalent Solids

16.1K
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...
16.1K
Convolution Properties I01:20

Convolution Properties I

576
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
576
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

350
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...
350

You might also read

Related Articles

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

Sort by
Same author

Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.

Ophthalmology science·2026
Same author

Real-CD: Change Detection Under Real-World Complex Interference via Dynamic Distribution Correction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

FA-Mamba: frequency attention driven Mamba for multimodal remote sensing classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Deep learning-based diagnostic classification of multiple sclerosis using multicenter optical coherence tomography data.

Experimental eye research·2026
Same author

Isfahan Artificial Intelligence Event 2024, Challenge I: Respiratory Depression Detection.

Journal of medical signals and sensors·2026
Same author

Diagnosing Multiple Sclerosis from Magnetic Resonance Imaging Images: Highlights from the Second Isfahan Artificial Intelligence Event 2024.

Journal of medical signals and sensors·2026
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026
Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

8.2K

Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks.

Ashkan Abbasi1, Amirhassan Monadjemi1, Leyuan Fang2

  • 1Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.

Computers in Biology and Medicine
|March 23, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-Input Fully-Convolutional Network (MIFCN) for denoising Optical Coherence Tomography (OCT) images. The method effectively reduces noise by fusing information from neighboring OCT images, improving image quality.

Keywords:
Fully convolutional network (FCN)Image denoisingMulti-input FCNOptical coherence tomography (OCT)

More Related Videos

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.9K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

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

Published on: August 4, 2018

9.0K

Related Experiment Videos

Last Updated: Jan 27, 2026

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

8.2K
Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.9K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

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

Published on: August 4, 2018

9.0K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Optical Coherence Tomography (OCT) images are susceptible to noise due to their formation process.
  • Noise in OCT images can hinder accurate diagnosis and analysis.
  • Existing denoising methods for natural images may not be optimal for OCT data.

Purpose of the Study:

  • To propose a novel deep learning-based method for denoising OCT images.
  • To leverage the unique properties of OCT data, such as correlations between adjacent images.
  • To enhance the quality of OCT images for improved clinical interpretation.

Main Methods:

  • Development of a Multi-Input Fully-Convolutional Network (MIFCN) architecture.
  • Pixel-by-pixel fusion of multiple FCNs to exploit correlations in neighboring OCT images.
  • Training the network by enforcing consistency between the overall output and individual input contributions.

Main Results:

  • The MIFCN method demonstrated superior performance in denoising OCT images compared to state-of-the-art techniques.
  • Quantitative and qualitative evaluations showed significant noise reduction.
  • The method proved effective on OCT images from both normal and age-related macular degeneration (AMD) affected eyes.

Conclusions:

  • The proposed MIFCN is an effective deep learning approach for OCT image denoising.
  • This method enhances the quality of OCT images, aiding in more accurate diagnostic assessments.
  • MIFCN offers a promising solution for improving low-level vision tasks in medical imaging.