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

  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Automated Segmentation And Source Prediction Of Bone Tumors Using Convnextv2 Fusion Based Mask R-cnn To Identify Lung Cancer Metastasis

Automated segmentation and source prediction of bone tumors using ConvNeXtv2 Fusion based Mask R-CNN to identify lung cancer metastasis

Ketong Zhao1,2, Ping Dai1, Ping Xiao3

  • 1Health Management Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518055, Guangdong Province, China.

Journal of Bone Oncology
|October 21, 2024

Related Experiment Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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

2.7K
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed an advanced 3D Mask R-CNN model with a ConvNeXt-V2 backbone for accurate lung cancer bone metastasis detection and segmentation. The model shows high performance, aiding personalized cancer treatment planning.

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Lung cancer is a leading cause of cancer mortality globally.
  • Bone metastasis from lung cancer significantly impacts patient quality of life and treatment complexity.
  • Accurate detection and segmentation of bone tumors are crucial for effective patient management.

Purpose of the Study:

  • To develop and validate an advanced 3D Mask R-CNN model for automatic bone tumor segmentation.
  • To identify lung cancer metastasis to the bone using a deep learning approach.
  • To support personalized treatment planning in clinical oncology.

Main Methods:

  • Utilized a 3D Mask R-CNN model integrated with a ConvNeXt-V2 backbone.
  • Employed high-resolution CT scans (1 mm slice thickness) from two hospitals for training and external validation.

Related Experiment Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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

2.7K
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K
  • Manual segmentation and radiologist validation of regions of interest (ROIs).

    • Main Results:

    • The model achieved a Dice Similarity Coefficient (DSC) of 0.849 on the external validation set for segmentation.
    • Achieved high sensitivity (0.911) and specificity (0.931) for bone tumor segmentation on the test set.
    • Demonstrated strong performance in classification with an Area Under the Curve (AUC) of 0.842 on the test set.

    Conclusions:

    • The developed 3D Mask R-CNN model shows significant potential for accurate bone tumor segmentation and lung cancer metastasis detection.
    • This AI-driven approach can enhance diagnostic workflows and contribute to personalized treatment strategies.
    • Further validation and integration into clinical practice could improve patient outcomes in oncology.