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

<i>CDH1</i> mutation-related breast cancer with multistage breast surgery-a case report.

AME case reports·2026
Same author

GRU-Based Deep Multimodal Fusion of Speech and Head-IMU Signals in Mixed Reality for Parkinson's Disease Detection.

Sensors (Basel, Switzerland)·2026
Same author

A Community Benchmark for the Automated Segmentation of Pediatric Neuroblastoma on Multi-Modal MRI: Design and Results of the SPPIN Challenge at MICCAI 2023.

Bioengineering (Basel, Switzerland)·2025
Same author

Analysis of Voice, Speech, and Language Biomarkers of Parkinson's Disease Collected in a Mixed Reality Setting.

Sensors (Basel, Switzerland)·2025
Same author

Benchmark of Deep Encoder-Decoder Architectures for Head and Neck Tumor Segmentation in Magnetic Resonance Images: Contribution to the HNTSMRG Challenge.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
Same author

Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.

Diagnostics (Basel, Switzerland)·2025

Related Experiment Video

Updated: Oct 31, 2025

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.6K

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed

Marek Wodzinski1, Izabela Ciepiela2, Tomasz Kuszewski3,4

  • 1Department of Measurement and Electronics, AGH University of Science and Technology, PL30059 Kraków, Poland.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study introduces a novel deep learning method for precise breast tumor bed localization after surgery. The technique improves real-time radiotherapy planning, reducing radiation exposure to healthy tissue.

Keywords:
breast-conserving surgerydeep learningimage registrationmissing dataradiotherapy

More Related Videos

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.8K
Spatial Measurements of Perfusion, Interstitial Fluid Pressure and Liposomes Accumulation in Solid Tumors
09:00

Spatial Measurements of Perfusion, Interstitial Fluid Pressure and Liposomes Accumulation in Solid Tumors

Published on: August 18, 2016

7.8K

Related Experiment Videos

Last Updated: Oct 31, 2025

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.6K
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.8K
Spatial Measurements of Perfusion, Interstitial Fluid Pressure and Liposomes Accumulation in Solid Tumors
09:00

Spatial Measurements of Perfusion, Interstitial Fluid Pressure and Liposomes Accumulation in Solid Tumors

Published on: August 18, 2016

7.8K

Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Breast-conserving surgery necessitates radiotherapy to prevent recurrence.
  • Accurate localization of the tumor bed for irradiation is challenging.
  • Image registration can improve localization and reduce healthy tissue irradiation.

Purpose of the Study:

  • To develop a novel deep learning-based nonrigid image registration method for breast tumor bed localization.
  • To address data loss from tumor resection in radiotherapy planning.
  • To enable real-time radiotherapy planning.

Main Methods:

  • A modified U-Net architecture for deep learning-based nonrigid image registration.
  • Multi-resolution processing to handle large deformations.
  • A volume penalty incorporating tumor resection knowledge.

Main Results:

  • The method achieved a mean target registration error below 6.5 mm.
  • The relative volume ratio was close to zero, indicating accurate volume preservation.
  • Registration time was under 1 second, enabling real-time application.
  • Improvements were shown over classical and other learning-based methods.

Conclusions:

  • The proposed method effectively localizes the breast tumor bed post-surgery.
  • It enhances real-time radiotherapy planning by accurately registering pre- and post-operative scans.
  • The approach reduces irradiation of surrounding healthy tissues, improving patient outcomes.