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

Convolution Properties II01:17

Convolution Properties II

587
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...
587
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

252
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
252
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
Convolution Properties I01:20

Convolution Properties I

599
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:
599
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
Factors Affecting Renal Clearance: Renal Impairment01:17

Factors Affecting Renal Clearance: Renal Impairment

463
Renal dysfunction significantly impairs the renal clearance of drugs, leading to potential complications in drug therapy. Renal failure, which can be caused by various factors, poses a significant challenge in the elimination of drugs from the body.
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...
463

You might also read

Related Articles

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

Sort by
Same author

Identifying and targeting abnormal mitochondrial localization associated with psychosis.

bioRxiv : the preprint server for biology·2026
Same author

Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarios.

Journal of neuroimaging : official journal of the American Society of Neuroimaging·2026
Same author

A two-step temporal data augmentation and supervised learning framework for predicting autism diagnosis at 36 months in patients with tuberous sclerosis complex.

Computers in biology and medicine·2026
Same author

Comparison of dynamic contrast-enhanced MRI versus MAG-3 scintigraphy for differential renal function assessment in pediatric patients.

Pediatric radiology·2026
Same author

Reply to Editorial Comment on "MR Urography Revealing Renal Physiology: Compensatory Changes in Duplex Kidneys".

Urology·2026
Same author

Comparison of Myeloarchitectonic Feature Recognition of the Primary Visual Cortex at 7 T Relative to 3 T MRI.

Journal of magnetic resonance imaging : JMRI·2026
Same journal

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

DUAL CROSS-ATTENTION SIAMESE TRANSFORMER FOR RECTAL TUMOR REGROWTH ASSESSMENT IN WATCH-AND-WAIT ENDOSCOPY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

AN IN SILICO STUDY OF LOW-INTENSITY FOCUSED ULTRASOUND DISPLACEMENT MAPPING WITH A 220 KHZ CLINICAL PHASED-ARRAY TRANSDUCER.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
See all related articles

Related Experiment Video

Updated: Feb 2, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K

AUTOMATIC RENAL SEGMENTATION IN DCE-MRI USING CONVOLUTIONAL NEURAL NETWORKS.

Marzieh Haghighi1,2, Simon K Warfield2, Sila Kurugol2

  • 1Electrical and Computer Engineering Department, Northeastern University, Boston, MA, 02115.

Proceedings. IEEE International Symposium on Biomedical Imaging
|November 27, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using 3D convolutional neural networks for segmenting pediatric kidneys in dynamic contrast-enhanced MRI (DCE-MRI) scans, achieving high accuracy in seconds for both normal and hydronephrotic kidneys.

Keywords:
CNNDCE-MRIFully-automatedKidney segmentation

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Related Experiment Videos

Last Updated: Feb 2, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pediatric Nephrology

Background:

  • Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for evaluating kidney function in children.
  • Accurate segmentation of renal parenchyma is essential for diagnosis and treatment planning.
  • Existing segmentation methods can be time-consuming and memory-intensive.

Purpose of the Study:

  • To develop a fully automated, time-efficient, and memory-efficient method for segmenting pediatric kidneys using DCE-MRI.
  • To achieve high segmentation accuracy for both normal and hydronephrotic kidneys.
  • To enable rapid kidney function evaluation in pediatric patients.

Main Methods:

  • A novel method employing a cascaded application of two 3D convolutional neural networks.
  • Utilizes spatial and temporal information from DCE-MRI for kidney localization and segmentation.
  • Evaluated on pediatric patients with normal kidneys and varying degrees of hydronephrosis.

Main Results:

  • Achieved high segmentation accuracy with a mean Dice coefficient of 91.4% for normal kidneys.
  • Demonstrated robust performance on abnormal kidneys, with a mean Dice coefficient of 83.6% for hydronephrosis.
  • The method operates efficiently, with running times in the order of seconds.

Conclusions:

  • The proposed automated segmentation method is accurate and efficient for pediatric kidney DCE-MRI.
  • This technique can significantly aid in the diagnosis and treatment of kidney diseases in children.
  • The approach offers a valuable tool for rapid clinical assessment of pediatric renal function.