Paul Drayna

ENDOGENOUS NOCARDIA ENDOPHTHALMITIS PRESENTING AS A MASS LESION IN A PATIENT WITH METATSTATIC NONSMALL CELL CARCINOMA OF THE LUNG.

Purpose: To report a case of Nocardia cyriacigeorgica endophthalmitis. Methods: Case report. Results: A 71-year-old man on long-term steroid therapy for poor respiratory function from lung carcinoma and presumed recent liver metastases presented with a four week history of blurry vision in the left eye. Ophthalmic examination revealed a white elevated mass in the macula with hemorrhage, concerning for metastasis. The patient was treated for presumed ocular metastases but had poor response to radiotherapy and was diagnosed four weeks later with N. cyriacigeorgica by retinal biopsy. Despite intravitreal and systemic antibiotics, the patient had progression of disease and died of respiratory involvement. Conclusion: Nocardia endogenous endophthalmitis can present as a mass retinal lesion in immunosuppressed patients with metastatic disease. Early vitreous and retinal biopsy may be required for definitive diagnosis and treatment. A pulmonary source of infection should be suspected and monitored closely.

Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain

A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain. A flattened RPE boundary is calculated such that all three types of fluid regions, intra-retinal, sub-retinal and sub-RPE, are located above it. 2) Seed points for fluid (object) and tissue (background) are initialized for graph cut by the proposed automated method. 3) A new cost function is proposed in kernel space, and is minimized with max-flow/min-cut algorithms, leading to a binary segmentation. Important properties of the proposed steps are proven and quantitative performance of each step is analyzed separately. The proposed method is evaluated using a publicly available dataset referred as Optima and a local dataset from the UMN clinic. For fluid segmentation in 2D individual slices, the proposed method outperforms the previously proposed methods by 18%, 21% with respect to the dice coefficient and sensitivity, respectively, on the Optima dataset, and by 16%, 11% and 12% with respect to the dice coefficient, sensitivity and precision, respectively, on the local UMN dataset. Finally, for 3D fluid volume segmentation, the proposed method achieves true positive rate (TPR) and false positive rate (FPR) of 90% and 0.74%, respectively, with a correlation of 95% between automated and expert manual segmentations using linear regression analysis.