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

Methods of Obtaining Topography

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...

Morphogenesis

Morphogenesis

Plant morphogenesis—the development of a plant’s form and structure—involves several overlapping developmental processes, including growth and cell differentiation. Precursor cells differentiate into specific cell types, which are organized into the tissues and organ systems that make up the functional plant.

Curvilinear Motion: Rectangular Components

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...

You might also read

Related Articles

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

Sort by

Same author

Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017

Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017

Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017

Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026

Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026

Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 29, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

Predicting infant cortical surface development using a 4D varifold-based learning framework and local

Islem Rekik¹, Gang Li¹, Weili Lin¹

¹Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.

Medical Image Analysis

|December 1, 2015

Summary

This summary is machine-generated.

We developed a novel predictive model for infant cortical surface development using spatiotemporal current-based learning. This method accurately forecasts brain shape evolution from birth to 12 months, improving upon previous techniques.

Keywords:

Cortical shape prediction Infant cortical surface Longitudinal brain development Surface topography Varifold metric

More Related Videos

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Related Experiment Videos

Last Updated: Mar 29, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Neuroimaging
Developmental Biology
Computational Anatomy

Background:

Longitudinal neuroimaging has advanced understanding of early brain development.
Predictive models for normal and abnormal cortical shape evolution are lacking.
Accurate prediction of infant brain development is crucial for early diagnosis and intervention.

Purpose of the Study:

To pioneer the first prediction model for longitudinal infant cortical surfaces.
To improve prediction accuracy by introducing novel varifold metric and topographic attribute morphing.
To capture both geometric and dynamic features of infant cortical surface development.

Main Methods:

Developed a spatiotemporal current-based learning framework using baseline cortical surface.
Introduced a varifold metric for improved surface registration and extended it to spatiotemporal regression.
Incorporated topographic attributes (normal direction, principal curvature sign) for baseline surface morphing.

Main Results:

The model accurately predicts cortical surface shapes at 3, 6, 9, and 12 months from birth data.
Achieved higher prediction accuracy compared to previous methods.
Successfully captured the complex spatiotemporal dynamics of the highly folded infant cortical surface.

Conclusions:

The proposed prediction model offers a significant advancement in understanding and forecasting infant brain development.
The use of varifold metric and topographic attributes enhances the model's predictive power.
This framework has potential applications in early detection of neurodevelopmental abnormalities.