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

Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

259
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
259
Dense Connective Tissue01:13

Dense Connective Tissue

12.2K
Dense connective tissue contains more collagen fibers than loose connective tissue. As a consequence, it displays greater resistance to stretching. There are two major categories of dense connective tissue— regular and irregular.
Dense Regular Connective Tissue
In dense regular connective tissue, fibers are arranged parallel to each other, enhancing its tensile strength and resistance to stretching in the direction of the fiber orientations. Ligaments and tendons are made of dense regular...
12.2K
Abdominal Aorta01:25

Abdominal Aorta

2.4K
Once the aorta traverses the diaphragmatic plane at the aortic hiatus, it is known as the abdominal aorta. This anatomical structure is positioned leftward of the spinal column, encased within a cocoon of adipose tissue behind the peritoneal cavity. It terminates at the L4 vertebra, where it splits into the common iliac arteries. Prior to this bifurcation, the abdominal aorta gives rise to several vital branches.
The celiac trunk, a singular artery, divides into the left gastric artery, which...
2.4K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Ultrasound I: Abdominal Ultrasonography01:20

Ultrasound I: Abdominal Ultrasonography

1.7K
Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Translational assessment instruments for preclinical to first-in-human decision-making: a scoping review.

Journal of translational medicine·2026
Same author

Practical regulatory guidance for researchers manufacturing customized 3D (three-dimensional) - printed medical devices.

Expert review of medical devices·2026
Same author

Language interventions for autistic people: An online survey of community member views and priorities.

JCPP advances·2026
Same author

ProBIOPSY: A Multidisciplinary International Consensus on Standards for Prostate Biopsy.

European urology·2026
Same author

Artificial intelligence-enabled MRI surveillance of prostate cancer: integrating PRECISE v2, PI-QUAL, and longitudinal biomarkers-a narrative review.

Abdominal radiology (New York)·2026
Same author

Surgical outcomes in gallbladder cancer: evidence from the UK nationwide CAPBIL study.

HPB : the official journal of the International Hepato Pancreato Biliary Association·2026

Related Experiment Video

Updated: Feb 8, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.4K

Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks.

Eli Gibson, Francesco Giganti, Yipeng Hu

    IEEE Transactions on Medical Imaging
    |July 12, 2018
    PubMed
    Summary

    A new deep-learning algorithm offers registration-free segmentation of abdominal organs on CT scans. This method achieves higher accuracy than existing techniques, improving image-guided navigation for endoscopic procedures.

    More Related Videos

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    815
    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
    09:21

    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

    Published on: February 18, 2015

    12.6K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
    10:25

    Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

    Published on: September 25, 2019

    49.4K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    815
    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
    09:21

    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

    Published on: February 18, 2015

    12.6K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Anatomy

    Background:

    • Accurate segmentation of abdominal anatomy in computed tomography (CT) is crucial for clinical workflows.
    • Traditional methods like multi-atlas label fusion (MALF) require challenging inter-subject registrations for abdominal images.
    • Existing registration-free methods have not consistently outperformed registration-based approaches for most abdominal organs.

    Purpose of the Study:

    • To develop and evaluate a novel registration-free deep-learning-based segmentation algorithm for eight key abdominal organs.
    • To assess the algorithm's accuracy compared to existing deep learning and MALF methods.
    • To determine the potential of this method for enhancing image-guided navigation in endoscopic pancreatic and biliary procedures.

    Main Methods:

    • A deep-learning algorithm was developed for the registration-free segmentation of the pancreas, gastrointestinal tract (esophagus, stomach, duodenum), liver, spleen, left kidney, and gallbladder.
    • The algorithm was validated using cross-validation on a multi-center dataset of 90 subjects.
    • Segmentation accuracy was directly compared against existing deep learning and MALF methods using Dice scores and mean absolute distances.

    Main Results:

    • The proposed deep-learning method achieved significantly higher Dice scores for all segmented organs compared to existing methods.
    • Specific improvements included Dice scores of 0.78 for the pancreas (vs. 0.71-0.74), 0.90 for the stomach (vs. 0.83-0.87), and 0.76 for the esophagus (vs. 0.66-0.69).
    • The new method also demonstrated lower mean absolute distances for most organs, indicating superior accuracy.

    Conclusions:

    • The developed deep-learning algorithm provides accurate, registration-free segmentation of multiple abdominal organs on CT images.
    • This approach surpasses the accuracy of current segmentation methods, including MALF and other deep learning techniques.
    • The findings suggest significant potential for this method to improve image-guided navigation in gastrointestinal endoscopy.