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

Sensory Modalities01:15

Sensory Modalities

3.9K
Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
3.9K
Taping Over Different Ground Profiles01:12

Taping Over Different Ground Profiles

381
Taping over varying ground profiles requires careful adaptation to achieve accurate measurements. On smooth, level ground with minimal vegetation, the tape can rest directly on the ground. Here, the taping team, typically consisting of a head and a rear tapeman, coordinates their positions with clear communication. The rear tapeman holds the tape at the starting point and guides the head tapeman toward a range pole placed beyond the endpoint, using hand or voice signals to ensure alignment.On...
381
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
Connective Tissue Fibers and Ground Substance01:17

Connective Tissue Fibers and Ground Substance

18.0K
One of the significant functions of connective tissue is connecting tissues and organs. Unlike epithelial tissue that is composed of cells closely packed with little or no extracellular space in between, connective tissue cells are dispersed in a matrix. The matrix usually includes a large amount of extracellular material produced by the connective tissue cells that are embedded within it. It plays a significant role in the functioning of this tissue. The major component of the matrix is a...
18.0K
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

178
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
178
Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

Opioid Analgesics: Synthetic and Semisynthetic Opioids

1.1K
Synthetic and semisynthetic opioids are pivotal in pain management and tackling opioid addiction. Semisynthetic opioids, including morphinans (morphine derivatives), oxycodone, oxymorphone, hydrocodone, and hydromorphone, have improved pharmacokinetic profiles compared to morphine. Additionally, heroin and 6-MAM (6-Monoacetylmorphine) show better CNS penetration than morphine due to heightened lipid solubility. Hydromorphone, a potent opioid, undergoes hepatic metabolism to form the active...
1.1K

You might also read

Related Articles

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

Sort by
Same author

A model to estimate spleen volume from palpated spleen length in patients with myelofibrosis.

Annals of hematology·2026
Same author

Add-on parsaclisib for patients with myelofibrosis and suboptimal response to ruxolitinib: a randomized phase 3 study.

The oncologist·2026
Same author

ADVERSARIAL SYNTHESIS LEARNING ENABLES SEGMENTATION WITHOUT TARGET MODALITY GROUND TRUTH.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

Sex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2024
Same author

Early adipose tissue wasting in a novel preclinical model of human lung cancer cachexia.

bioRxiv : the preprint server for biology·2024
Same author

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

ArXiv·2024

Related Experiment Video

Updated: Feb 3, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.8K

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth.

Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon

    IEEE Transactions on Medical Imaging
    |October 19, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces SynSeg-Net, a novel method for medical image segmentation that trains models for new imaging types without manual labels. This deep learning approach enhances generalizability in medical imaging analysis.

    More Related Videos

    A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
    07:40

    A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

    Published on: May 27, 2021

    4.6K
    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
    04:25

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

    Published on: December 15, 2023

    3.8K

    Related Experiment Videos

    Last Updated: Feb 3, 2026

    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
    08:05

    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

    Published on: December 19, 2020

    14.8K
    A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
    07:40

    A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

    Published on: May 27, 2021

    4.6K
    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
    04:25

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

    Published on: December 15, 2023

    3.8K

    Area of Science:

    • Medical Imaging
    • Deep Learning
    • Computer Vision

    Background:

    • Deep convolutional neural networks (DCNNs) for image segmentation struggle with generalizability across different imaging modalities and patient cohorts.
    • Manual annotation of training data is labor-intensive and time-consuming, posing a significant bottleneck in developing robust segmentation models.
    • Transferring segmentation capabilities from one imaging modality (e.g., MRI) to another (e.g., CT) without target-modality labels is a critical challenge.

    Purpose of the Study:

    • To develop an end-to-end synthetic segmentation network (SynSeg-Net) capable of training segmentation models for a target imaging modality using only source-modality manual labels.
    • To alleviate the need for manual tracing in new imaging scenarios, thereby reducing annotation efforts.
    • To improve the generalizability of deep learning-based medical image segmentation.

    Main Methods:

    • The proposed SynSeg-Net leverages unpaired image data from source and target modalities.
    • It integrates recent advancements in Cycle Generative Adversarial Networks (CycleGAN) with deep convolutional neural networks (DCNNs).
    • The network is trained using manual labels exclusively from the source modality to generate synthetic segmentations for the target modality.

    Main Results:

    • SynSeg-Net demonstrated superior performance compared to traditional two-stage methods in cross-modality segmentation tasks.
    • Evaluated on MRI to CT splenomegaly segmentation and CT to MRI total intracranial volume (TICV) segmentation, the method showed significant effectiveness.
    • In specific scenarios, SynSeg-Net achieved performance comparable to conventional segmentation networks that utilized target-modality labels.

    Conclusions:

    • The end-to-end SynSeg-Net effectively addresses the generalizability limitation of DCNN-based segmentation by enabling training without target-modality manual labels.
    • This approach offers a viable solution for reducing manual annotation burden in medical image segmentation.
    • The publicly available source code facilitates further research and application of this novel technique.