Papilledema is a particular type of optic disc swelling caused by elevated intracranial pressure. By observing the visible features from fundus images or direct funduscopic examination, a typical method of assessing papilledema (i.e., the six-stage Fris\'en grading system) is qualitative and frequently suffers from low reproducibility.
Compared to fundus images, spectral-domain optical coherence tomography (SD-OCT) is a relatively new imaging technique and enables the cross-sectional information of the retina to be acquired. Using SD-OCT images, quantitative measurements like evaluating the retinal volume or depth are intuitively more robust than the traditional qualitative approach to evaluate papilledema. Also, multiple studies suggest that the deformation of the peripapillary retinal pigment epithelium and/or Bruch's membrane (pRPE/BM) may reflect the intracranial pressure change. In other words, modeling/quantifying the pRPE/BM shape can potentially be another indicator of papilledema. However, when the optic disc is severely swollen, the retinal structure is dramatically deformed and often causes the commercial SD-OCT devices to fail to segment the retinal layers. Without appropriate layer segmentation, all the retinal measurements are not reliable.
To solve the current issue of inconsistently assessing papilledema severity, a comprehensive machine-learning framework is proposed in this doctoral work to achieve the goal by accomplishing following four aims. First, robust approaches are developed to automatically segment the retinal layers in 2D and 3D SD-OCT images, even though the optic discs can be severely swollen. Second, the semi- and fully automated methodologies are designed to segment the pRPE/BM opening under the swollen inner retina in these SD-OCT images. Third, the pRPE/BM shape models are constructed using both 2D and 3D SD-OCT images, and then the 2D/3D pRPE/BM shape measures are computed. Finally, based on the previously segmented retinal layers, eight OCT 2D/3D global/local measurements of retinal structure are reliably computed. Considering both the 2D/3D pRPE/BM shape measures and these eight OCT features as an input set, a machine-learning framework using the random forest technique is proposed to compute a papilledema severity score (PSS) on a continuous scale. The newly proposed PSS is expected to be an alternative to the traditional qualitative method to provide a more objective measurement of assessing papilledema severity.