Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

Computed Tomography

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...

You might also read

Related Articles

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

Sort by

Same author

Proteomic composition and mutual assembly of the C2a projection in vertebrate motile cilia.

eLife·2026

Same author

An ionic liquid-based photoresist with expandable applications: direct patterning from small molecules via thiol-ene photopolymerization.

Journal of colloid and interface science·2026

Same author

Microtubule-associated CCDC112 is essential for spermiogenesis and male fertility in mice.

Journal of molecular cell biology·2026

Same author

Low-pressure superconducting properties and the regulation mechanism of the ternary hydride Li<sub>2</sub>PbH<sub>4</sub>.

Physical chemistry chemical physics : PCCP·2026

Same author

Postpartum ovarian vein thrombosis: plain CT with clinical indicators-diagnostic performance and clinical application.

BMC pregnancy and childbirth·2026

Same author

3D Segment Anything Model With Visual Mamba for Diagnosing Placenta Accreta Spectrum.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Search research articles

Related Experiment Video

Updated: Jul 10, 2025

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

Anatomical Prior-Based Automatic Segmentation for Cardiac Substructures from Computed Tomography Images.

Xuefang Wang¹, Xinyi Li², Ruxu Du³

¹Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511400, China.

Bioengineering (Basel, Switzerland)

|November 25, 2023

Summary

This study introduces a deep learning framework for accurate cardiac substructure segmentation using computed tomography (CT) scans. The novel approach enhances diagnostic capabilities by improving the precision of identifying cardiac anatomy.

Keywords:

CT anatomical knowledge cardiac substructure segmentation deep learning medical image segmentation

More Related Videos

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens

Published on: April 18, 2013

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

Related Experiment Videos

Last Updated: Jul 10, 2025

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens

Published on: April 18, 2013

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Cardiac substructure segmentation is crucial for diagnosis and treatment.
Computed tomography (CT) offers noninvasive cardiac evaluation.
Challenges include diverse grayscales, fuzzy boundaries, and variable locations of cardiac substructures.

Purpose of the Study:

To develop a deep learning framework for accurate automatic segmentation of cardiac substructures.
To improve segmentation accuracy by integrating cardiac anatomical knowledge.
To address challenges posed by varying scales and complex features of cardiac substructures.

Main Methods:

A deep learning framework integrating cardiac anatomical knowledge was designed.
A coarse-to-fine cascaded network processed structures of different scales.
Prior segmentation results and anatomical knowledge guided the fine segmentation of smaller substructures.

Main Results:

The framework achieved efficient and accurate segmentation of ten cardiac substructures.
Demonstrated significantly higher segmentation accuracy compared to mainstream models.
Successfully segmented small cardiac targets within multi-target segmentation tasks.

Conclusions:

The proposed framework, fused with prior anatomical knowledge, shows superior segmentation performance.
This approach enhances the accuracy of cardiac substructure segmentation in CT images.
It offers a promising tool for improved cardiac diagnosis and treatment planning.