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

You might also read

Related Articles

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

Sort by

Same author

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026

Same author

Robust quantification of ICG fluorescence perfusion in neonatal bowel surgery via deep point tracking.

International journal of computer assisted radiology and surgery·2026

Same author

Blob representation of robotic surgical scenes for position-aware video generation.

International journal of computer assisted radiology and surgery·2026

Same author

VerTE-MT: A Multi-Task Framework with Entropy-Guided Sampling for Vertebrae Segmentation and Localisation in CT.

IEEE journal of biomedical and health informatics·2026

Same author

SurgViVQA: temporally grounded video question answering for surgical scene understanding.

International journal of computer assisted radiology and surgery·2026

Same author

A benchmark of methods for surgical instrument segmentation in endoscopic pituitary surgery.

International journal of computer assisted radiology and surgery·2026

Same journal

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026

Same journal

BronchoLumen: analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy.

International journal of computer assisted radiology and surgery·2026

Same journal

Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).

International journal of computer assisted radiology and surgery·2026

Same journal

Electromagnetic navigation for femoral osteotomy using high-accuracy X-ray-to-CT registration.

International journal of computer assisted radiology and surgery·2026

Same journal

Modular instrument actuation unit for robotic-assisted systems in laparoscopic surgery.

International journal of computer assisted radiology and surgery·2026

Same journal

Pose-aware deep perceptual similarity for iterative 2D/3D registration of knee joints using contrastive learning.

International journal of computer assisted radiology and surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 29, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

PseudoSegRT: efficient pseudo-labelling for intraoperative OCT segmentation.

Yu Huang¹, Riaz Asaria^2,3, Danail Stoyanov^1,2

¹Department of Computer Science, University College London, London, UK.

International Journal of Computer Assisted Radiology and Surgery

|May 26, 2023

Summary

This summary is machine-generated.

A new semi-supervised deep learning method uses pseudo-labelling for retinal optical coherence tomography (OCT) segmentation, significantly improving robotic ophthalmic surgery guidance with minimal labelled data.

Keywords:

Deep learning, Pseudo-labelling Real-time OCT segmentation Robotic microsurgery Semi-supervised learning

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Related Experiment Videos

Last Updated: Jul 29, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Area of Science:

Ophthalmic microsurgery
Medical imaging
Deep learning

Background:

Robotic ophthalmic microsurgery offers improved precision for complex procedures.
Intraoperative optical coherence tomography (iOCT) aids visualization, but deep learning segmentation requires extensive labeled data.

Purpose of the Study:

To develop a semi-supervised method for retinal OCT boundary segmentation to guide robotic surgery.
To overcome the limitations of time-consuming data annotation in deep learning for ophthalmic surgery.

Main Methods:

A U-Net based model utilizing a pseudo-labelling strategy combining labeled and unlabeled OCT scans.
Training the model with a semi-supervised approach to enhance segmentation accuracy.
Optimization and acceleration of the model using TensorRT for real-time performance.

Main Results:

The pseudo-labelling method achieved better generalizability and performance on unseen data compared to fully supervised methods, using only 2% of labeled samples.
Accelerated GPU inference achieved speeds under 1 millisecond per frame with FP16 precision.

Conclusions:

Semi-supervised pseudo-labelling strategies show potential for real-time OCT segmentation in guiding robotic surgical systems.
The accelerated network is promising for precise segmentation of OCT images and guiding surgical tools for sub-retinal injections.