Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Convolution Properties II

Convolution Properties II

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...

Protein Networks

Protein Networks

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,...

Protein Networks

Protein Networks

Convolution Properties I

Convolution Properties I

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:

Network Covalent Solids

Network Covalent Solids

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...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

You might also read

Related Articles

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

Sort by

Same author

Machine-learning-based cardiovascular mortality prediction using a cumulative PM<sub>2.5</sub> exposure metric in high risk groups: a retrospective cohort study.

Scientific reports·2026

Same author

Detecting cardiovascular diseases using ECG scans and explainable artificial intelligence.

Computer methods and programs in biomedicine·2026

Same author

DnaA binding to oriCII regulates transcription from Pseudomonas aeruginosa gidAB-parAB locus linked to the chromosome segregation and streptomycin resistance.

Scientific reports·2026

Same author

Diagnostic accuracy of 1H-MRS in detecting the oncometabolite 2-hydroxyglutarate (2HG) in adult-type diffuse gliomas.

Journal of neuro-oncology·2026

Same author

Positron-emission tomography as a predictor of response to first-line cyclin-dependent kinase 4/6 inhibitors in patients with metastatic ER-positive HER2-negative breast cancer.

NPJ breast cancer·2026

Same author

Air, Noise, and Light Pollution and Thromboembolic Cardiovascular Complications: A TH Scientific Statement.

Thrombosis and haemostasis·2026

Search research articles

Related Experiment Video

Updated: Jan 23, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks.

Pablo Ribalta Lorenzo¹, Jakub Nalepa², Barbara Bobek-Billewicz³

¹Future Processing, Bojkowska 37A, 44-100 Gliwice, Poland.

Computer Methods and Programs in Biomedicine

|June 16, 2019

Summary

This study introduces a novel deep learning method for accurate brain tumor segmentation from MRI scans, outperforming existing techniques. The approach is efficient, even with small, varied datasets, enabling faster diagnosis and treatment planning.

Keywords:

Brain tumor Deep neural network Image segmentation MRI

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

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

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

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jan 23, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

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

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

Published on: December 15, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Magnetic resonance imaging (MRI) is crucial for brain tumor diagnosis.
Accurate brain tumor segmentation is vital for treatment planning but remains challenging due to data variability.
High-quality annotations for training are costly and time-consuming.

Purpose of the Study:

To develop a novel deep learning approach for accurate brain tumor segmentation from MRI.
To enable training with small, heterogeneous datasets.
To accelerate MRI analysis for clinical applications.

Main Methods:

Utilized fully convolutional neural networks (FCNNs).
Employed a battery of data augmentation techniques to enhance robustness.
Trained models using only positive (tumorous) examples due to data limitations.

Main Results:

The deep learning approach outperformed state-of-the-art methods using hand-crafted features in segmentation accuracy.
Achieved very fast training and near real-time segmentation (under one second per image).
Demonstrated robustness with small, imbalanced, and heterogeneous datasets.

Conclusions:

The proposed deep learning method offers superior performance compared to traditional approaches.
The network effectively handles challenging datasets and provides rapid inference.
This technique can significantly aid in the clinical workflow for brain tumor patients.