Advanced OCTA imaging segmentation: Unsupervised, non-linear retinal vessel detection using modified self-organizing maps and joint MGRF modeling.

BACKGROUND AND OBJECTIVE: 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. METHODS: 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. 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. CONCLUSIONS: 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.

Traumatic hemorrhage and chain of survival.

Trauma is the number one cause of death among Americans between the ages of 1 and 46 years, costing more than $670 billion a year. Following death related to central nervous system injury, hemorrhage accounts for the majority of remaining traumatic fatalities. Among those with severe trauma that reach the hospital alive, many may survive if the hemorrhage and traumatic injuries are diagnosed and adequately treated in a timely fashion. This article aims to review the recent advances in pathophysiology management following a traumatic hemorrhage as well as the role of diagnostic imaging in identifying the source of hemorrhage. The principles of damage control resuscitation and damage control surgery are also discussed. The chain of survival for severe hemorrhage begins with primary prevention; however, once trauma has occurred, prehospital interventions and hospital care with early injury recognition, resuscitation, definitive hemostasis, and achieving endpoints of resuscitation become paramount. An algorithm is proposed for achieving these goals in a timely fashion as the median time from onset of hemorrhagic shock and death is 2 h.