Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Computed Tomography01:10

Computed Tomography

8.9K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
8.9K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

410
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
410
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
What is an Electrochemical Gradient?01:26

What is an Electrochemical Gradient?

128.6K
Adenosine triphosphate, or ATP, is considered the primary energy source in cells. However, energy can also be stored in the electrochemical gradient of an ion across the plasma membrane, which is determined by two factors: its chemical and electrical gradients.
The chemical gradient relies on differences in the abundance of a substance on the outside versus the inside of a cell and flows from areas of high to low ion concentration. In contrast, the electrical gradient revolves around an...
128.6K
Leaky Scanning02:28

Leaky Scanning

5.7K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.7K
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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Outcomes and Prognostic Factors of Auto-Transplanted Immature Third Molars: A 15-Year Retrospective Cohort Study.

International dental journal·2026
Same author

Cyphenothrin disrupts the integrity of porcine trophectoderm and uterine luminal epithelial cell lines by inducing mitochondrial dysfunction and oxidative stress.

Chemosphere·2026
Same author

Corrigendum to "Automatic liver Couinaud segmentation from computed tomography scans with a gradient-enhanced hierarchical cascade deep learning network" [Current Problems in Surgery 75 (2026) 101957].

Current problems in surgery·2026
Same author

Tannic acid-loaded mesoporous silica particles as tissue adhesives for enhanced wound healing.

Biomaterials science·2026
Same author

A Selective Deposition Strategy of Ultrathin Metal Layer on Sub-Micrometer-Pitch Cu Interconnection for Low-Temperature Hybrid Bonding.

Small science·2026
Same author

A brief guide to isolating primary hepatocytes from mice.

Molecules and cells·2026

Video Experimental Relacionado

Updated: Feb 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.5K

Segmentación de Tumores Hepáticos Basada en Aprendizaje Profundo a partir de Tomografías Computarizadas con una Red

Hangyeul Shin1, Kyujin Han2, Seungyoo Lee3

  • 1School of Applied Artificial Intelligence and Entrepreneurship, Handong Global University, Pohang 37554, Republic of Korea.

Diagnostics (Basel, Switzerland)
|February 13, 2026
PubMed
Resumen

Este estudio presenta un método de segmentación automatizada de tumores hepáticos utilizando la red G-UNETR++. El enfoque logró una alta precisión, superando a los modelos existentes para mejorar el diagnóstico del cáncer de hígado.

Palabras clave:
tomografía computarizadaaprendizaje profundored mejorada por gradientesegmentación de tumores hepáticos

Más Videos Relacionados

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K
DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis
12:39

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

Published on: September 28, 2021

3.8K

Videos de Experimentos Relacionados

Last Updated: Feb 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.5K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K
DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis
12:39

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

Published on: September 28, 2021

3.8K

Área de la Ciencia:

  • Imagenología Médica
  • Inteligencia Artificial
  • Oncología

Sus antecedentes:

  • Los tumores hepáticos plantean importantes desafíos diagnósticos.
  • La segmentación precisa es crucial para una planificación de tratamiento eficaz.
  • Los métodos de segmentación existentes a menudo requieren intervención manual.

Objetivo del estudio:

  • Desarrollar un método de segmentación de tumores hepáticos completamente automático.
  • Aprovechar la red mejorada por gradiente G-UNETR++.
  • Mejorar las capacidades de diagnóstico del cáncer de hígado.

Principales métodos:

  • Se utilizó G-UNETR++ para la segmentación de hígado y tumores en tomografías computarizadas.
  • Se implementó una estrategia de enmascaramiento para enfocar la segmentación en la región del hígado.
  • Se entrenó y validó el modelo en los conjuntos de datos LiTS y 3DIRCADb.

Principales resultados:

  • Se logró una puntuación Dice promedio de 0.844 en el conjunto de datos LiTS.
  • Se obtuvo una puntuación Dice promedio de 0.832 en el conjunto de datos 3DIRCADb.
  • Se superaron los modelos de vanguardia en la segmentación de tumores hepáticos.

Conclusiones:

  • El método desarrollado basado en G-UNETR++ ofrece una segmentación automática eficaz de tumores hepáticos.
  • El enfoque demuestra una fuerte generalización en diferentes conjuntos de datos.
  • Esta herramienta puede ayudar a los médicos en el diagnóstico y la planificación del tratamiento de tumores hepáticos.