Translating the speckle-based segmentation approach to in-vivo clinical settings poses several challenges and limitations. One major challenge is the increased variability of ocular structures in live patients compared to ex-vivo models. In-vivo imaging introduces motion artifacts, changes in tissue properties due to physiological factors, and variations in patient anatomy, all of which can impact the speckle patterns in OCT images. This variability can make it challenging to accurately segment retinal layers and surgical tools, as the statistical models may not generalize well to the diverse range of speckle patterns encountered in clinical practice.
Another limitation is the need for real-time processing and feedback in clinical settings. The computational complexity of fitting statistical distributions and optimizing parameters may hinder the speed and efficiency required for intraoperative applications. Delays in segmentation could impact the accuracy of surgical guidance systems and the overall success of the procedure.
Furthermore, the generalizability of the segmentation model to different OCT devices and imaging protocols is crucial for clinical adoption. Variations in device specifications, image acquisition settings, and noise levels can affect the performance of the segmentation algorithm, requiring robust validation and adaptation strategies to ensure consistent results across different clinical environments.