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

A Vision Transformer-Based Deep Learning Framework for Patient-Level Classification of Acute Pancreatitis and Normal Pancreas Using Computed Tomography.

Diagnostics (Basel, Switzerland)·2026
Same author

Signet-Ring Cell Colorectal Cancer and Signet-Ring Cell Component Colorectal Cancer: Do They Differ in Clinical Behavior?

Journal of clinical medicine·2026
Same author

An In Vitro Study on the Efficacy of Green Synthesized Silver Nanoparticles on Surgical Site Infections and Healings.

Biomedicines·2026
Same author

Fully Automated Segmentation of Cervical Spinal Cord in Sagittal MR Images Using Swin-Unet Architectures.

Journal of clinical medicine·2025
Same author

Development and Proof-of-Concept Evaluation of a Structured Reporting Template for Emergency Radiology Using Synthetic Cases.

Diagnostics (Basel, Switzerland)·2025
Same author

A Novel Radiology-Adapted Logistic Model for Non-Invasive Risk Stratification of Pigmented Superficial Skin Lesions: A Methodological Pilot Study.

Diagnostics (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jun 7, 2025

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

MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation.

Emre Dandıl1, Betül Tiryaki Baştuğ2, Mehmet Süleyman Yıldırım3

  • 1Department of Computer Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, 11230 Bilecik, Türkiye.

Diagnostics (Basel, Switzerland)
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces MaskAppendix, a deep learning model for accurate appendix segmentation in CT scans. It achieves state-of-the-art results, improving diagnostic accuracy for appendicitis and related conditions.

Keywords:
CT imagingDetectronappendix segmentationdeep learninggrad-CAMmask R-CNN

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

369
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Related Experiment Videos

Last Updated: Jun 7, 2025

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
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

369
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Appendicitis is a common cause of emergency abdominal surgery, necessitating accurate diagnosis.
  • Automated appendix segmentation in medical imaging is challenging due to anatomical variability.

Purpose of the Study:

  • To develop a precise appendix segmentation method for computed tomography (CT) scans.
  • To enhance diagnostic accuracy and clinical workflow efficiency for appendicitis.

Main Methods:

  • Proposed a backbone-enriched Mask R-CNN architecture (MaskAppendix) using ResNet101.
  • Integrated Gradient-weighted Class Activation Mapping (Grad-CAM) for improved feature localization.
  • Utilized the Detectron platform for implementation.

Main Results:

  • Achieved state-of-the-art performance in appendix segmentation on abdominal CT scans.
  • Obtained a Dice Similarity Coefficient (DSC) score of 87.17% for automatic segmentation.
  • Demonstrated superior accuracy and robustness compared to traditional methods.

Conclusions:

  • The MaskAppendix framework offers an effective tool for aiding clinicians in diagnosing appendicitis.
  • The method has the potential to reduce diagnostic errors and improve clinical workflow.
  • This AI-driven approach enhances the precision of medical image analysis for abdominal conditions.