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

You might also read

Related Articles

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

Sort by
Same author

MultiTask learning AI system to assist BCC diagnosis with dual explanation.

Scientific reports·2026
Same author

Concordance in Basal Cell Carcinoma Diagnosis. Building a Proper Standard Reference to Train Artificial Intelligence Tools.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2025
Same author

Advancing infantile hemangioma diagnosis by integrating temperature, color, and texture.

Journal of biomedical optics·2025
Same author

Nanostructured fibrin-agarose hydrogels loaded with allogeneic fibroblasts as bio-dressings for acute treatment of massive burns.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2024
Same author

Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients.

Clinical and translational radiation oncology·2023
Same author

Thermography as a Method for Bedside Monitoring of Infantile Hemangiomas.

Cancers·2022

Related Experiment Video

Updated: Mar 7, 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

3.6K

Validation of a method for retroperitoneal tumor segmentation.

Cristina Suárez-Mejías1, José A Pérez-Carrasco2, Carmen Serrano3

  • 1Technological Innovation Group, Virgen del Rocío University Hospital, Avda Manuel Siurot, s/n, 41013, Sevilla, Spain.

International Journal of Computer Assisted Radiology and Surgery
|February 12, 2017
PubMed
Summary

A new semiautomatic segmentation tool accurately delimits retroperitoneal tumors on CT images, significantly reducing processing time compared to manual methods. This advancement aids physicians in surgical and radiotherapy planning.

Keywords:
CTContinuous convex relaxationRadiotherapy planningRetroperitoneal tumorSurgical planning

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

837
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.7K

Related Experiment Videos

Last Updated: Mar 7, 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

3.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

837
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.7K

Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Surgical Planning

Background:

  • Current surgical planning applications, including AYRA, struggle with accurate soft tissue tumor segmentation.
  • Accurate delimitation of retroperitoneal tumors is crucial for effective radiotherapy and surgical planning.

Purpose of the Study:

  • To validate a semiautomatic segmentation tool for accurate retroperitoneal tumor delimitation from CT images.
  • To provide physicians with an improved tool for surgical and radiotherapy planning.

Main Methods:

  • Manual segmentation of 11 retroperitoneal tumor cases by 6 experts.
  • Comparison of the proposed algorithm's segmentation with AYRA, Pinnacle, expert manual segmentation, and expert-delimited tumors.
  • Validation of the semiautomatic algorithm against established methods.

Main Results:

  • The proposed algorithm demonstrated accurate segmentation of retroperitoneal tumors.
  • The algorithm achieved an average 90.5% reduction in computational time compared to manual contouring.
  • Minimal computational time was required for the segmentation process.

Conclusions:

  • The developed semiautomatic retroperitoneal tumor segmentation method is thoroughly validated.
  • Incorporating this algorithm could significantly enhance surgical and radiotherapy planning tools like AYRA.