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

Shanghai international consensus on diagnosis and comprehensive treatment of colorectal liver metastases (version 2019).

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2020
Same author

Stimuli-Responsive Delivery of Growth Factors for Tissue Engineering.

Advanced healthcare materials·2020
Same author

Systematic prediction of the biological functions of TAS2R10 using positive co-expression analysis.

Experimental and therapeutic medicine·2020
Same author

Gelatin Methacryloyl Microneedle Patches for Minimally Invasive Extraction of Skin Interstitial Fluid.

Small (Weinheim an der Bergstrasse, Germany)·2020
Same author

Take it seriously or not: postoperative pneumocephalus in CSDH patients?

British journal of neurosurgery·2020
Same author

No Differences in the Prevalence and Intensity of Chronic Postsurgical Pain Between Laparoscopic Hysterectomy and Abdominal Hysterectomy: A Prospective Study.

Journal of pain research·2020
Same journal

Acoustic Characterization of a Modified IEC Agar-Based Tissue-Mimicking Material Across the 3.5-50 MHz Frequency Range.

Ultrasound in medicine & biology·2026
Same journal

Deep Learning-Based Standard Section Recognition and Multi-Organ Segmentation in Upper Abdominal Ultrasound.

Ultrasound in medicine & biology·2026
Same journal

Cardiac Natural Mechanical Wave Detection and Speed Estimation Using Deep Learning-Based 2-D Ultrasound Imaging: A Feasibility Study.

Ultrasound in medicine & biology·2026
Same journal

Region-Specific Evaluation of Plaque Segmentation in Cross-sectional Projections of Carotid Ultrasound Images Using Deep Learning Models in a Sub-clinical Atherosclerosis Cohort.

Ultrasound in medicine & biology·2026
Same journal

Simulating the Dedifferentiation Process of Thyroid Cancer: Insights from Mouse Models and Ultrasound Imaging.

Ultrasound in medicine & biology·2026
Same journal

A Nomogram Based on Ultrasound Features for Predicting Major Intra-Operative Hemorrhage in Patients With Placenta Accreta Spectrum (PAS).

Ultrasound in medicine & biology·2026
See all related articles

Related Experiment Video

Updated: Dec 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

872

Calcification segmentation based on a different scales superpixels saliency detection algorithm.

Li Ren1, Yangyang Liu2, Ying Tong2

  • 1Electronic and Communication Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China 210003.

Ultrasound in Medicine & Biology
|September 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for detecting breast tumor calcifications in ultrasound images. The method effectively segments both strong and weak calcifications, improving early breast cancer detection accuracy.

Keywords:
Benign tumorCalcification detectionMalignant tumorSuperpixel. Segmentation

More Related Videos

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.1K
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

3.2K

Related Experiment Videos

Last Updated: Dec 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

872
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.1K
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

3.2K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Accurate detection of breast tumor calcifications is crucial for early breast cancer diagnosis.
  • Current methods may face challenges in segmenting calcifications of varying characteristics.

Purpose of the Study:

  • To develop and evaluate a superpixel saliency detection algorithm for segmenting calcifications in breast tumor ultrasound images.
  • To enhance the accuracy of early breast cancer detection through improved calcification identification.

Main Methods:

  • Utilized a multi-scale saliency segmentation algorithm to extract weak calcifications (Wca) based on image features.
  • Employed single-scale superpixel segmentation and feature analysis to generate a strong calcification extraction map.
  • Combined strong and weak calcification maps to obtain the final calcification detection map.

Main Results:

  • The proposed algorithm effectively segmented calcifications in breast ultrasound images.
  • Successfully extracted both weak and strong calcifications using distinct image analysis techniques.
  • Demonstrated the potential for improved diagnostic assistance in breast cancer screening.

Conclusions:

  • The developed algorithm offers an effective approach for detecting breast tumor calcifications in ultrasound.
  • This technique can aid clinicians in improving the accuracy of early breast cancer detection.
  • The method shows promise for integration into clinical diagnostic workflows.