Automatic quantification of choroidal neovascularization lesion area on OCT angiography based on density cell-like P systems with active membranes

Abstract

Detecting and quantifying the size of choroidal neovascularization (CNV) is important for the diagnosis and assessment of neovascular age-related macular degeneration. Depth-resolved imaging of the retinal and choroidal vasculature by optical coherence tomography angiography (OCTA) has enabled the visualization of CNV. However, due to the prevalence of artifacts, it is difficult to segment and quantify the CNV lesion area automatically. We have previously described a saliency algorithm for CNV detection that could identify a CNV lesion area with 83% accuracy. However, this method works under the assumption that the CNV region is the most salient area for visual attention in the whole image and consequently, errors occur when this requirement is not met (e.g. when the lesion occupies a large portion of the image). Moreover, saliency image processing methods cannot extract the edges of the salient object very accurately. In this paper, we propose a novel and automatic CNV segmentation method based on an unsupervised and parallel machine learning technique named density cell-like P systems (DEC P systems). DEC P systems integrate the idea of a modified clustering algorithm into cell-like P systems. This method improved the accuracy of detection to 87.2% on 22 subjects and obtained clear boundaries of the CNV lesions.

An update on inflammatory choroidal neovascularization: epidemiology, multimodal imaging, and management

“…To overcome the inherent limitations of OCTA, modern techniques such as machine learning algorithms can enable more sophisticated image analyses and interpretation. For instance, machine learning algorithms can allow identification of CNV by automated segmentation, improving the accuracy of detection, permitting quantification, and delineating the boundaries of the lesion [ 55 ]. In the future, such techniques may be employed in the diagnosis of i-CNV.…”

Projection-Resolved Optical Coherence Tomographic Angiography of Retinal Plexuses in Retinitis Pigmentosa

“…Our group has recently developed projection-resolved OCTA (PR-OCTA) algorithm 33,34 to significantly suppress projection artifacts, allowing for proper segmentation of retinal circulation, and in turn more reliable visualization and quantification of microvasculature. [35][36][37][38][39][40][41][42][43][44][45] In this study, we aim to characterize the alterations in perfusion in all three retinal plexuses, as well as the relationship between vascular and structural changes in RP patients.…”

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

“…. , w q are the multisets of objects placed in the regions of the membranes, with w i ∈ O, for 1 ≤ i ≤ q; R represents multiple but finite sets of evolution rules associated with the membranes; R ′ represents multiple but finite sets of communication rules between different membranes; and i 0 is the output region or output membrane in the P system [45].…”