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

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

59.6K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
59.6K
Force Classification01:22

Force Classification

2.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.4K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.4K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.4K
Classification of Leukocytes01:30

Classification of Leukocytes

6.1K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
6.1K
Classification of Illness01:17

Classification of Illness

8.8K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.8K
Classification of Bones01:18

Classification of Bones

10.1K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
10.1K

You might also read

Related Articles

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

Sort by
Same author

Automated Detection and Assessment of Post-Operative Eyelid Outcome in Trachomatous Trichiasis Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

BIBSNet: A deep learning baby image brain segmentation network for MRI scans.

Developmental cognitive neuroscience·2026
Same author

Infant subcortical brain volumes associated with maternal obesity and diabetes: a large multicohort human study.

BMC medicine·2026
Same author

Increased CSF volume, altered brain development and emotional reactivity after postnatal Zika virus infection in infant rhesus macaques.

bioRxiv : the preprint server for biology·2026
Same author

Long-term associations between perinatal factors and white matter microstructure at 8-10 years.

Frontiers in human neuroscience·2026
Same author

Maternal depression with and without a history of childhood maltreatment and newborn white matter microstructure.

Psychological medicine·2026
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

528

TRAFIC: Fiber Tract Classification Using Deep Learning.

Prince D Ngattai Lam1, Gaetan Belhomme1, Jessica Ferrall1

  • 1NIRAL, UNC, Chapel Hill, North Carolina, United States.

Proceedings of Spie--The International Society for Optical Engineering
|May 22, 2018
PubMed
Summary
This summary is machine-generated.

TRAFIC is a new automated tool that labels and classifies brain fiber tracts using a neural network. This method speeds up analysis for medical applications, offering encouraging results in fiber tract classification.

Keywords:
ClassificationDTIDWIdeep learningdiffusionfibersneural networkstractography

More Related Videos

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Related Experiment Videos

Last Updated: Feb 10, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

528
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Manual classification of brain fiber tracts is time-consuming and requires specialized anatomical knowledge.
  • Existing methods for fiber tract analysis often rely on Diffusion Tensor Imaging (DTI) atlases, which can be limiting.
  • Automating the classification of brain white matter tracts is crucial for advancing neuroimaging research and clinical applications.

Purpose of the Study:

  • To introduce TRAFIC, a fully automated tool for labeling and classifying brain fiber tracts.
  • To develop a method independent of DTI atlases, utilizing shape features from pre-traced fibers.
  • To improve the efficiency and accuracy of brain fiber tract analysis in medical applications.

Main Methods:

  • TRAFIC employs a neural network trained on shape features derived from manually corrected fiber tracts.
  • The system classifies new fibers based on the learned features, without requiring a DTI atlas.
  • Shape features are computed from already traced fiber tracts for training and classification.

Main Results:

  • Encouraging results were achieved in the classification of traced fiber tracts using the TRAFIC approach.
  • The automated tool demonstrated proficiency in labeling and classifying complex brain fiber structures.
  • The method proved effective in overcoming the limitations of manual classification and atlas-dependent techniques.

Conclusions:

  • TRAFIC offers a significant advancement in the automated analysis of brain fiber tracts.
  • The tool streamlines a previously laborious process, making it more accessible for medical applications.
  • This automated approach holds promise for enhancing neuroimaging studies and clinical diagnostics.