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Correction propagation for user-assisted optical coherence tomography segmentation: general framework and application
Daniel Stromer1,2,3, Eric M Moult1,3, Siyu Chen1
1Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.
Biomedical Optics Express
|June 6, 2020
Summary
This study introduces a user-assisted segmentation method for optical coherence tomography (OCT) imaging. This approach improves the accuracy of segmenting retinal features, particularly Bruch's membrane in age-related macular degeneration.
Area of Science:
- Ophthalmology
- Medical Imaging
- Biomedical Engineering
Background:
- Optical coherence tomography (OCT) is a key ophthalmic imaging technique.
- Interest in en face OCT visualization and analysis has grown due to higher A-scan rates and OCT angiography (OCTA).
- Accurate segmentation of retinal features in OCT data is crucial for quantitative analysis but challenging due to image variability and pathologies.
Purpose of the Study:
- To develop and evaluate a user-assisted segmentation approach for OCT data.
- To reduce the manual correction burden for automatic segmentation algorithms.
- To assess the efficacy of this approach for Bruch's membrane segmentation in age-related macular degeneration.
Main Methods:
- Development of a user-assisted segmentation method incorporating correction propagation.
- Complementary approach to fully-automatic segmentation techniques.
- Evaluation focused on segmenting Bruch's membrane in OCT scans of patients with advanced age-related macular degeneration.
Main Results:
- The user-assisted approach effectively reduces the labor involved in correcting automatic segmentations.
- Demonstrated feasibility for segmenting Bruch's membrane in challenging clinical cases.
- Correction propagation aids in refining segmentation accuracy.
Conclusions:
- User-assisted segmentation offers a practical solution to enhance the reliability of OCT data analysis.
- This method can improve the efficiency and accuracy of segmenting complex retinal structures.
- It holds promise for clinical applications, especially in diseases like age-related macular degeneration.
