研究目的
To present an automated and robust GA segmentation method based on object tracking strategy for time series SD-OCT volumetric images, reducing the need for manual segmentation by experts.
研究成果
The proposed automated and robust GA segmentation method shows good agreement with manual segmentations, demonstrating high correlation coefficients and overlap ratios. It offers a more efficient and accurate alternative to traditional and deep learning methods for GA segmentation in SD-OCT images.
研究不足
The method requires initial manual calibration of GA lesion area for the first moment of each patient, which may still involve some level of expert intervention.
1：Experimental Design and Method Selection:
The method involves preprocessing (denoising and layer segmentation), a new sample construction method for extracting HOG features, and training a random forest model for GA segmentation.
2：Sample Selection and Data Sources:
SD-OCT cubes from 10 eyes in 7 patients with GA were used.
3：List of Experimental Equipment and Materials:
Cirrus HD-OCT device for generating SD-OCT cubes.
4：Experimental Procedures and Operational Workflow:
Preprocessing steps include noise removal and layer segmentation, followed by sample construction, HOG feature extraction, and random forest model training for GA segmentation.
5：Data Analysis Methods:
Quantitative evaluation using correlation coefficient (cc), absolute area difference (ADD), and overlap ratio (Overlap) metrics.
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