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

You might also read

Related Articles

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

Sort by

Same author

High mobility group box 1 (HMGB1) levels in the placenta and in serum in preeclampsia.

American journal of reproductive immunology (New York, N.Y. : 1989)·2011

Same author

Destabilization of coxsackievirus b3 genome integrated with enhanced green fluorescent protein gene.

Intervirology·2011

Same author

[Clinicopathological features of primary splenic histiocytic sarcoma: a case report and literature review].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2011

Same author

[Comparison of treatment with micro endoscopic discectomy and posterior lumbar interbody fusion using single and double B-Twin expandable spinal spacer].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2011

Same author

Virtual transplantation in designing a facial prosthesis for extensive maxillofacial defects that cross the facial midline using computer-assisted technology.

The International journal of prosthodontics·2011

Same author

Total synthesis of phorboxazole A via de novo oxazole formation: convergent total synthesis.

Journal of the American Chemical Society·2010

Same journal

Posterior capsule rupture with complete lens dislocation into the vitreous cavity caused by blunt trauma: a case report.

Frontiers in medicine·2026

Same journal

Case Report: Heparin resistance as the harbinger of heparin-induced thrombocytopenia in acute pulmonary embolism.

Frontiers in medicine·2026

Same journal

Trends and variation in use of end-tidal carbon dioxide during in-hospital cardiac arrest: an observational cohort study.

Frontiers in medicine·2026

Same journal

From virtual pregnancy to digital twin obstetrics: multimodal data integration for personalized prediction of pregnancy complications.

Frontiers in medicine·2026

Same journal

Immunotherapy with or without low-intensity chemotherapy versus conventional chemotherapy as first-line treatment for newly diagnosed B-ALL patients fit for intensive chemotherapy: a propensity score-matched study.

Frontiers in medicine·2026

Same journal

Hypertension and frailty in older adults: a bibliometric analysis and knowledge mapping based on Web of Science, Scopus, and PubMed (1973-2025).

Frontiers in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 2, 2025

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

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

Published on: November 30, 2022

Accurate Tumor Segmentation via Octave Convolution Neural Network.

Bo Wang^1,2,3, Jingyi Yang⁴, Jingyang Ai³

¹The State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China.

Frontiers in Medicine

|June 7, 2021

Summary

This summary is machine-generated.

This study introduces an efficient 3D liver tumor segmentation method using octave convolutions for improved accuracy in CT images. The novel approach enhances computer-aided diagnosis and treatment planning for liver cancer patients.

Keywords:

deep learning liver liver tumor octave convolution segmentation

More Related Videos

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Related Experiment Videos

Last Updated: Nov 2, 2025

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

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

Published on: November 30, 2022

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

3D liver tumor segmentation from CT images is crucial for cancer diagnosis and treatment.
Accurate segmentation remains challenging despite extensive research.
Existing methods struggle with varying tumor sizes and shapes.

Purpose of the Study:

To develop an effective and efficient 3D liver tumor segmentation method for CT images.
To improve the accuracy and speed of liver tumor segmentation.
To enhance computer-aided diagnosis, treatment planning, and monitoring of liver cancer.

Main Methods:

Utilized encoder-decoder based octave convolution networks for feature extraction.
Employed octave convolutions to capture multi-spatial-frequency features of tumors.
Integrated a deep supervision mechanism for faster convergence and improved discrimination.
Adapted the UNet architecture with octave convolutions for end-to-end learning.

Main Results:

The proposed method significantly outperformed existing networks in accuracy measures.
Achieved superior processing speed compared to other segmentation approaches.
Demonstrated effective capture of tumors with diverse sizes and shapes.
Generated high-resolution tumor segmentations without post-processing.

Conclusions:

The proposed octave convolution-based method offers an effective and efficient solution for 3D liver tumor segmentation in CT images.
This advancement can significantly benefit computer-aided diagnosis and treatment planning in liver cancer care.
The deep supervision mechanism and octave convolutions contribute to faster convergence and enhanced discrimination capabilities.