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Updated: Dec 16, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
An unsupervised hierarchical approach for automatic intra-retinal cyst segmentation in spectral-domain optical
Razieh Ganjee1, Mohsen Ebrahimi Moghaddam1, Ramin Nourinia2
1The Faculty of Computer Science and Engineering, Shahid Beheshti University G.C, Tehran, Iran.
Purpose:
Intra-retinal cyst (IRC) is a symptom of macular disorders that occurs due to retinal blood vessel damage and fluid leakage to the macula area. These abnormalities are efficiently visualized using optical coherence tomography (OCT) imaging. These patients need to be regularly monitored for the presence and changes of IRC regions. Thus, automatic segmentation of IRCs can be beneficial to investigate disease progression.
Methods:
In this study, automatic IRC segmentation is accomplished by building three different masks in three unsupervised segmentation levels of a hierarchical framework. In the first level, the ROI-mask (R-mask) is built, and the retina area is cropped based on this mask. In the second level, the prune-mask (P-mask) is built, and the searching space is significantly reduced toward the target objects using this mask; and finally in the third level, by applying the Markov random field (MRF) model and employing intensity and contextual information, the cyst mask (C-mask) is extracted.
Results:
The proposed method is evaluated on three datasets including OPTIMA, UMN, and KERMANY datasets. The experimental results showed that the proposed method is effective with a mean dice coefficient rate of 0.74, 0.75 and 0.79 by the intersection of ground truths on the OPTIMA, UMN and KERMANY datasets, respectively.
Conclusion:
The proposed method outperforms the state-of-the-art methods on the OPTIMA and UMN datasets while achieving comparable results to the most recently proposed method on the KERMANY dataset.
