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

Mass Analyzers: Overview01:13

Mass Analyzers: Overview

1.8K
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
1.8K
Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

1.6K
The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
1.6K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

976
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
976
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

1.6K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.6K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

325
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
325
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

6.9K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
6.9K

You might also read

Related Articles

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

Sort by
Same author

Distributed control circuits across a brain-and-cord connectome.

Nature·2026
Same author

A multi-resolution imaging and analysis pipeline for comparative circuit reconstruction in insects.

bioRxiv : the preprint server for biology·2026
Same author

Uncertainty quantification for connectomics.

Nature methods·2025
Same author

Sexual dimorphism in the complete connectome of the <i>Drosophila</i> male central nervous system.

bioRxiv : the preprint server for biology·2025
Same author

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context.

bioRxiv : the preprint server for biology·2025
Same author

Distributed control circuits across a brain-and-cord connectome.

bioRxiv : the preprint server for biology·2025
Same journal

Fleeing is believing: adaptive behavior under social threat as an inference process.

Frontiers in neural circuits·2026
Same journal

A modular and flexible pipeline for intraoperative electrode reconstruction and localization in patients with brain lesions.

Frontiers in neural circuits·2026
Same journal

Functional implications of atypical action potential generation in the (patho)physiological brain: from developmental program to glioma.

Frontiers in neural circuits·2026
Same journal

Loss of function of Noggin inhibits glial scar formation and motor function recovery after spinal cord injury.

Frontiers in neural circuits·2026
Same journal

Cross domain consistency of aesthetic preference-driven social behavior.

Frontiers in neural circuits·2026
Same journal

Beyond synaptic plasticity: a summary of a linear model of the cerebellar locomotor computation.

Frontiers in neural circuits·2026
See all related articles

Related Experiment Video

Updated: Feb 2, 2026

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
11:03

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Published on: November 10, 2015

10.0K

Analyzing Image Segmentation for Connectomics.

Stephen M Plaza1, Jan Funke1

  • 1Howard Hughes Medical Institute, Ashburn, VA, United States.

Frontiers in Neural Circuits
|November 29, 2018
PubMed
Summary
This summary is machine-generated.

New metrics and a scalable software framework improve large-scale electron microscope image segmentation for connectomics. This approach enhances evaluation by focusing on downstream analysis and connectivity, addressing limitations of current methods.

Keywords:
connectomicselectron microscopyevaluationimage segmentationmetrics

More Related Videos

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.8K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.5K

Related Experiment Videos

Last Updated: Feb 2, 2026

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
11:03

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Published on: November 10, 2015

10.0K
A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.8K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.5K

Area of Science:

  • Neuroscience
  • Computer Science
  • Biophysics

Background:

  • Automatic image segmentation is crucial for electron microscope (EM) connectome reconstruction.
  • Current segmentation evaluation metrics and competitions (e.g., CREMI, SNEMI) have limitations for large-scale connectomics datasets.
  • Existing metrics do not sufficiently emphasize downstream analysis impact or connectivity information.

Purpose of the Study:

  • To develop a novel strategy for evaluating EM image segmentation at large scales, both supervised and unsupervised.
  • To introduce new metrics aligned with downstream analysis and reconstruction, focusing on connectivity and segmentation failures.
  • To present a scalable software framework for integrating and visualizing these new metrics.

Main Methods:

  • Development of novel synapse connectivity and completeness metrics.
  • Introduction of measures for segmentation correctness and completeness (orphan fragments, self-loops) computable without ground truth.
  • Creation of a scalable, flexible software framework for metric integration, evaluation, and debugging.
  • Development of visualization tools for interpreting segmentation metrics.

Main Results:

  • The proposed metrics provide more meaningful interpretations of segmentation quality related to neuron connectivity.
  • Measures of orphan fragments and self-loops offer insights into segmentation failures without ground truth.
  • The software framework enables scalable evaluation and debugging of segmentation differences.
  • Evaluation on large public datasets yielded novel insights into segmentation performance.

Conclusions:

  • The novel metrics and software framework significantly advance the evaluation of large-scale EM image segmentation for connectomics.
  • This approach offers a more practical and insightful assessment of segmentation quality, crucial for reconstructing neural circuits.
  • The developed tools facilitate improved algorithm development and debugging in the field.