Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

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

Sort by

Same author

An Archaeal Cyclodextrin Glycosyltransferase from <i>Haloferax</i> sp.: Characterization and Application in Starch Degradation.

International journal of molecular sciences·2026

Same author

Regulation of Neuronal Senescence by <i>Srebf2</i> and <i>Zmiz1</i> Reveals Mechanisms of Aging-Related Neurodegeneration.

Biology·2026

Same author

Localized amyloid-β oligomers in the lateral entorhinal cortex drives olfactory dysfunction through aberrant neuronal and network activity.

Acta neuropathologica communications·2026

Same author

A quantitative and precision‑oriented neuronal reconstruction approach based on data grading.

Brain informatics·2026

Same author

A Million-Scale, Difficulty-Stratified Optical-Microscopy Image Annotation for Neuron Reconstruction.

Scientific data·2026

Same author

Sympathetic immunomodulation suppresses liver metastasis by maintaining the balance between norepinephrine concentration and the α2a/β2-AR ratio.

Cellular & molecular immunology·2026

Same journal

Generalizable framework for multi-site bone density prediction using non-dominant wrist optical biomarkers.

Biomedical optics express·2026

Same journal

Erratum: Review of dynamic optical coherence tomography for intracellular motility [Invited]: errata.

Biomedical optics express·2026

Same journal

Digital-micromirror-device-based illumination strategies for background suppression in single-molecule localization microscopy.

Biomedical optics express·2026

Same journal

Synergistic combination of convective self-assembly and hollow core fiber for sensitive SERS detection of glucose molecules.

Biomedical optics express·2026

Same journal

Multimodal diagnostic network integrating infrared and mass spectra for lung cancer.

Biomedical optics express·2026

Same journal

Multimodal Optical Biosensing for Precision Medicine and Healthcare: Introduction to the feature issue.

Biomedical optics express·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 15, 2026

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

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

Published on: April 13, 2013

Brain-SAM: a general automatic SAM-based segmentation model for brain science images.

Shilong Zhang¹, Peicong Gong¹, Hong Zhang¹

¹State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya 572025, China.

Biomedical Optics Express

|February 16, 2026

Summary

This summary is machine-generated.

Brain-SAM enhances microscopic image segmentation using a novel approach based on the Segment Anything Model (SAM). This automated method achieves superior accuracy and efficiency in biomedical imaging tasks.

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jun 15, 2026

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

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

Published on: April 13, 2013

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Biomedical imaging
Computer vision
Microscopy

Background:

Microscopic optical image segmentation is crucial but challenging.
The Segment Anything Model (SAM) shows promise for natural image segmentation.

Purpose of the Study:

To develop Brain-SAM, an automated segmentation model for microscopic optical images.
To improve the efficiency, accuracy, and robustness of image segmentation in biomedicine.

Main Methods:

Utilized the Segment Anything Model (SAM) as a foundation.
Introduced an automatic prompt encoder for high-throughput segmentation.
Developed a segmentation optimizer to boost performance.

Main Results:

Brain-SAM outperformed specialized models on most tasks across eight benchmark datasets.
Achieved high IoU and Dice scores on Brain (98.07%, 99.03%), Tek (93.13%, 96.44%), and Lectin3d (88.49%, 93.89%) datasets.
Released new, publicly available brain science image datasets.

Conclusions:

Brain-SAM offers a powerful, automated solution for microscopic image segmentation.
The model demonstrates significant potential for advancing biomedical research and analysis.
Publicly available datasets will facilitate further research in brain science imaging.