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

Data Validation01:15

Data Validation

1.8K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
1.8K
Data Validation01:03

Data Validation

6.8K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

317
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
317
Reliability and Validity01:29

Reliability and Validity

13.9K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
13.9K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

420
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
420
Synthetic Biology02:55

Synthetic Biology

5.6K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
5.6K

You might also read

Related Articles

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

Sort by
Same author

Reinforcement Learning May Demystify the Limited Human Motor Learning Efficacy Due to Visual-Proprioceptive Mismatch.

International journal of neural systems·2024
Same author

Proportional sway-based electrotactile feedback improves lateral standing balance.

Frontiers in neuroscience·2024
Same author

AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit's activation via adjoint operators.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Editorial: Functional microcircuits in the brain and in artificial intelligent systems.

Frontiers in computational neuroscience·2023
Same author

AdjointBackMap: Reconstructing effective decision hypersurfaces from CNN layers using adjoint operators.

Neural networks : the official journal of the International Neural Network Society·2022
Same author

A Queryable Graph Representation of Vascular Connectivity in the Whole Mouse Brain.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020

Related Experiment Video

Updated: Feb 2, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

Data-Driven Synthetic Cerebrovascular Models For Validation Of Segmentation Algorithms.

Nowak Michael R, Yoonsuck Choe

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
    Summary

    This study presents a new method for creating realistic synthetic cerebrovasculature models. These models enable better evaluation of algorithms designed to segment blood vessels in medical images.

    More Related Videos

    A Data-Driven Approach to Quantifying Immune States in Sepsis
    07:42

    A Data-Driven Approach to Quantifying Immune States in Sepsis

    Published on: February 7, 2025

    543
    Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends
    12:20

    Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends

    Published on: March 15, 2014

    14.9K

    Related Experiment Videos

    Last Updated: Feb 2, 2026

    Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
    07:11

    Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

    Published on: November 10, 2023

    3.3K
    A Data-Driven Approach to Quantifying Immune States in Sepsis
    07:42

    A Data-Driven Approach to Quantifying Immune States in Sepsis

    Published on: February 7, 2025

    543
    Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends
    12:20

    Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends

    Published on: March 15, 2014

    14.9K

    Area of Science:

    • Medical imaging
    • Computational biology
    • Neuroscience

    Background:

    • Accurate segmentation of cerebrovasculature is crucial for diagnosing and treating neurological conditions.
    • Existing methods for evaluating vascular segmentation algorithms often lack biologically realistic datasets.

    Purpose of the Study:

    • To develop a novel, data-driven method for generating biologically grounded synthetic cerebrovasculature models.
    • To provide a robust framework for assessing the accuracy of vascular segmentation algorithms.

    Main Methods:

    • Obtaining vascular centerlines from imaging volumes using segmentation algorithms.
    • Reconstructing synthetic imaging volumes from graph-encoded centerlines to create ground truth.
    • Applying segmentation algorithms to synthetic volumes for accuracy assessment.

    Main Results:

    • Generated synthetic cerebrovasculature models that are biologically grounded and data-driven.
    • Enabled quantitative assessment of segmentation algorithm accuracy using known ground truth.
    • Ensured synthetic data reflects topological and geometrical characteristics of real vasculature.

    Conclusions:

    • The developed method offers a means for enhanced evaluation of vascular segmentation algorithms.
    • Biologically grounded synthetic models improve the reliability and validity of algorithm assessment.
    • This approach facilitates advancements in medical image analysis for cerebrovascular research.