Three-Dimensional Shape Modeling and Analysis of Brain Structures

Jaeil Kim; Maria  del Carmen Vald&#233;s Hern&#225;ndez; Jinah Park

doi:10.3791/59172

JoVE Journal
Neuroscience

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JoVE Journal Neuroscience

Three-Dimensional Shape Modeling and Analysis of Brain Structures

뇌 구조의 3차원 형상 모델링 및 분석

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05:33 min

November 14, 2019

DOI: 10.3791/59172-v

Jaeil Kim¹, Maria del Carmen Valdés Hernández², Jinah Park³

¹School of Computer Science and Engineering,Kyungpook National University, ²Centre for Clinical Brain Sciences,University of Edinburgh, ³School of Computing and KI for Health Science and Technology (KIHST),Korea Advanced Institute of Science and Technology (KAIST)

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Overview

This study introduces a semi-automatic protocol for 3D shape analysis of brain structures, focusing on hippocampal segmentation from brain MRI images. The methodology involves open software for image segmentation followed by group-wise shape analysis using an automated modeling package.

Key Study Components

Area of Science

Neuroscience
Image Analysis
Structural Brain Modeling

Background

Accurate shape recovery is essential for anatomical correspondence in brain models.
The framework includes tools for shape modeling and deformity computation.
Used with large human brain datasets for various studies.
The software was developed by Dr. Jaeil Kim and demonstrates user-friendly features.

Purpose of Study

To demonstrate a procedure for hippocampal segmentation and shape analysis.
To provide an automated framework for modeling individual and group brain shapes.
To offer tools for statistical analysis of shape variations.

Methods Used

The method utilizes a graphic user interface for MRI image and segmentation editing.
The study focuses on hippocampal structures using T1-weighted magnetic resonance images.
Users manually edit hippocampal segmentations and construct group templates.
Statistical analysis is performed using shape deformity measurements.
MATLAB code is provided for analysis at the project page.

Main Results

The approach allows for precise shape modeling of the hippocampus and computation of shape deformities.
Results demonstrate differences in hippocampal shape between groups with varying brain tissue volumes.
Individual shape characteristics are restored while minimizing distortion during modeling.
Visualization of aligned models and average shape deformity maps is included.

Conclusions

This protocol enables effective shape analysis and modeling of hippocampal structures.
The methodology enhances understanding of anatomical variations in brain research.
Applications extend to clinical studies involving conditions like Alzheimer's disease and other structural anomalies.

Frequently Asked Questions

What are the advantages of using this protocol for shape analysis?

This semi-automatic protocol enhances accuracy in shape modeling while reducing user effort through automation. It provides a robust framework for large datasets.

How is the hippocampal segmentation performed?

Segmentation begins with automatic results from the MRI, followed by manual editing to ensure that critical structures like the uncus are included in the mask.

What types of data can be obtained from this analysis?

The analysis yields detailed shape models, deformation measurements, and average shape deformity maps that reveal structural differences among populations.

Can the method be adapted for other brain structures?

Yes, while focused on the hippocampus, the framework can be applied to other brain structures requiring similar shape analysis methods.

What are the key considerations for using this approach?

Users should remain involved in critical steps that require confirmation, such as adjusting intensity parameters to fit segmentation results accurately.

How is statistical analysis integrated into this protocol?

Statistical analysis is performed on shape deformities to explore variations and correlations relevant to clinical conditions and patient populations.

개방형 소프트웨어를 사용한 이미지 세분화, 자동화된 모델링 패키지를 사용한 그룹 별 모양 분석 등 뇌 구조에 대한 형상 분석을 위한 반자동 프로토콜을 소개합니다. 여기서, 우리는 뇌 MR 이미지로부터 해마 세분화를 가진 3D 형상 분석 프로토콜의 각 단계를 시연한다.

거칠고 시끄러운 세분화에 대한 모양 특징을 정확하게 복구하는 것은 개별 뇌 모양 모델 간의 좋은 해부학 적 대응을 달성하는 데 중요합니다. 당사의 프레임워크는 개별 형상 모델링, 그룹 별 템플릿 구성 및 모양 기형 계산을 위한 다양한 도구를 제공합니다. 그리고 그것은 인간의 두뇌의 큰 데이터 집합에 사용 되었습니다.

이 절차를 시연하는 것은 뇌 모양 모델링용 소프트웨어를 개발한 실험실의 전 대학원 생인 김재일 박사가 될 것입니다. 해마 세분화의 수동 편집을 위해 T1 중량 자기 공명 이미지를 열고 자동 해마 세분화결과 그래픽 사용자 인터페이스 소프트웨어가 생성됩니다. 디스플레이 창의 아이콘을 클릭하여 관상 뷰를 선택하고 커미티드가 배치될 때까지 볼륨을 스크롤합니다.

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