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

You might also read

Related Articles

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

Sort by

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 author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017

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

Search research articles

Related Experiment Video

Updated: Jan 4, 2026

In Utero Electroporation of Multiaddressable Genome-Integrating Color MAGIC Markers to Individualize Cortical Mouse Astrocytes

In Utero Electroporation of Multiaddressable Genome-Integrating Color MAGIC Markers to Individualize Cortical Mouse Astrocytes

Published on: May 21, 2020

SPHERICAL U-NET FOR INFANT CORTICAL SURFACE PARCELLATION.

Fenqiang Zhao^1,2, Shunren Xia¹, Zhengwang Wu²

¹Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, China.

Proceedings. IEEE International Symposium on Biomedical Imaging

|November 5, 2019

Summary

This summary is machine-generated.

This study introduces a novel deep learning method for brain MRI analysis, enabling accurate cortical surface parcellation. The spherical U-Net effectively segments neonatal brain surfaces, outperforming existing methods.

Keywords:

Surface parcellation spherical U-Net

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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 4, 2026

In Utero Electroporation of Multiaddressable Genome-Integrating Color MAGIC Markers to Individualize Cortical Mouse Astrocytes

In Utero Electroporation of Multiaddressable Genome-Integrating Color MAGIC Markers to Individualize Cortical Mouse Astrocytes

Published on: May 21, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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:

Neuroimaging
Medical Image Analysis
Deep Learning

Background:

Accurate parcellation of cortical surfaces in human brain MRI is crucial for anatomical and functional region identification.
Existing methods may lack the precision required for detailed analysis of brain structures.

Purpose of the Study:

To propose a novel end-to-end deep learning method for cortical surface parcellation.
To adapt convolutional neural networks (CNNs) for spherical data representation.

Main Methods:

Formulating surface parcellation as a semantic segmentation task on the sphere.
Developing spherical convolution, pooling, and upsampling operations for surface meshes.
Transforming U-Net and SegNet architectures into spherical representations for neonatal cortical surface parcellation.

Main Results:

The proposed spherical U-Net demonstrated effectiveness and efficiency in neonatal cortical surface parcellation.
Comparison showed superior performance of the spherical U-Net over the spherical SegNet and a patch-wise classification method.
The method successfully applied deep learning techniques to spherical surface meshes.

Conclusions:

The developed spherical U-Net provides an effective and efficient approach for neonatal cortical surface parcellation.
This deep learning framework advances the analysis of brain MRI data by enabling precise segmentation of spherical cortical surfaces.
The study highlights the potential of spherical CNNs in neuroimaging applications.