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Related Experiment Video

Updated: Jul 21, 2025

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

Annotation-efficient learning for OCT segmentation.

Haoran Zhang¹, Jianlong Yang¹, Ce Zheng²

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Biomedical Optics Express

|July 27, 2023

Summary

This summary is machine-generated.

This study introduces an efficient deep learning method for Optical Coherence Tomography (OCT) segmentation, significantly reducing data annotation needs. The approach achieves comparable accuracy with less data and faster training times, enhancing OCT technology applications.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep learning models for OCT segmentation demand extensive data annotation.
Annotation is time-consuming and costly, limiting OCT applications in areas like surgical navigation and multi-center trials.

Purpose of the Study:

To develop an annotation-efficient learning method for OCT segmentation.
To reduce the costs and time associated with data annotation for OCT segmentation models.

Main Methods:

Leveraged self-supervised generative learning with a Transformer-based model for OCT imagery.
Integrated a Transformer encoder with a CNN decoder for dense pixel-wise prediction.
Introduced a k-center algorithm for selective data annotation.

Main Results:

Achieved comparable segmentation accuracy to U-Net using only a fraction of the training data.
Demonstrated up to 3.5x faster training times.
Outperformed other annotation efficiency strategies.

Conclusions:

The proposed method significantly reduces annotation costs and training time for OCT segmentation.
The pre-trained model adapts to diverse datasets and ROIs without re-training.
This learning efficiency can enhance OCT technology intelligence and adoption.