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 Experiment Video

Updated: Jan 7, 2026

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

3.3K

A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.

Dina Koishiyeva1, Dinargul Mukhammejanova2, Jeong Won Kang3

  • 1School of Information Technology and Engineering, Kazakh-British Technical University, Almaty 050000, Kazakhstan.

Bioengineering (Basel, Switzerland)
|December 30, 2025
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026

The Segment Anything Model (SAM) revolutionizes medical image segmentation with universal architectures. SAM derivatives achieve high accuracy (81-95% Dice) while significantly reducing annotation needs (56-73%).

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Medical image segmentation has evolved significantly due to foundational models.
  • The Segment Anything Model (SAM) marks a paradigm shift towards universal architectures in medical visualization.
  • Task-specific models are being replaced by more generalizable approaches.

Purpose of the Study:

  • To review the adaptation and impact of the Segment Anything Model (SAM) in medical visualization.
  • To explore SAM's application in multimodal fusion, volumetric extensions, and uncertainty-aware architectures.
  • To analyze the performance and annotation efficiency of SAM derivatives in medical contexts.

Main Methods:

  • Review of multimodal fusion frameworks for semantic alignment of visual data.
Keywords:
domain adaptationhybrid architecturesmedical segmentationsegment anything modeltuning

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

721

Related Experiment Videos

Last Updated: Jan 7, 2026

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

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

721
  • Analysis of volumetric extensions (e.g., SAM3D, ProtoSAM-3D, VISTA3D) for 3D spatial reasoning.
  • Examination of uncertainty-aware architectures (e.g., SAM-U, E-Bayes SAM) for clinical interpretability.

    • Main Results:

    • SAM derivatives demonstrate high segmentation accuracy, achieving Dice coefficients of 81-95%.
    • These models significantly reduce annotation requirements by 56-73%.
    • Adaptations focus on multimodal fusion, 3D processing, and probabilistic calibration.

    Conclusions:

    • SAM-based approaches offer a powerful and efficient solution for medical image segmentation.
    • Future work should focus on adaptive domain hints, Bayesian self-correction, and unified volumetric frameworks.
    • These advancements aim for autonomous generalization across diverse medical imaging applications.