Advanced OCTA imaging segmentation: Unsupervised, non-linear retinal vessel detection using modified self-organizing maps and joint MGRF modeling - Alksas, Ahmed; Sharafeldeen, Ahmed; Balaha, Hossam Magdy; Haq, Mohammad Z.; Mahmoud, Ali; Ghazal, Mohamed; Alghamdi, Norah Saleh; Alhalabi, Marah; Yousaf, Jawad; Sandhu, Harpal; El-Baz, Ayman (Computer methods and programs in biomedicine, 2024)
This paper proposes a fully automated and unsupervised stochastic segmentation approach using two-level joint Markov-Gibbs Random Field (MGRF) to detect the vascular system from retinal Optical Coherence Tomography Angiography (OCTA) images, which is a critical step in developing Computer-Aided Diagnosis (CAD) systems for detecting retinal diseases. Using a new probabilistic model based on a Linear Combination of Discrete Gaussian (LCDG), the first level models the appearance of OCTA images and their spatially smoothed images. The parameters of the LCDG model are estimated using a modified Expectation Maximization (EM) algorithm. The second level models the maps of OCTA images, including the vascular system and other retina tissues, using MGRF with analytically estimated parameters from the input images. The proposed segmentation approach employs modified self-organizing maps as a MAP-based optimizer maximizing the joint likelihood and handles the Joint MGRF model in a new, unsupervised way. This approach deviates from traditional stochastic optimization approaches and leverages non-linear optimization to achieve more accurate segmentation results. The proposed segmentation framework is evaluated quantitatively on a dataset of 204 subjects. Achieving 0.92 ± 0.03 Dice similarity coefficient, 0.69 ± 0.25 95-percentile bidirectional Hausdorff distance, and 0.93 ± 0.03 accuracy, confirms the superior performance of the proposed approach. The conclusions drawn from the study highlight the superior performance of the proposed unsupervised and fully automated segmentation approach in detecting the vascular system from OCTA images. This approach not only deviates from traditional methods but also achieves more accurate segmentation results, demonstrating its potential in aiding the development of CAD systems for detecting retinal diseases. •Applied fully automated, unsupervised learning for retinal OCTA images segmentation.•Employed a two-level joint MGRF and LCDG model to improve segmentation accuracy.•Enhanced the model’s precision and reliability with a modified EM algorithm.•Optimized joint likelihood using self-organizing maps in an unsupervised manner.•Demonstrated superior performance in segmenting OCTA retinal tissues.