Glaucoma is a common eye disease. It causes damage to patient’s vision and is difficult to diagnose. By locating Bruch’s membrane opening (BMO) in the Optical Coherence Tomography (OCT) image we can compute important diagnostic parameters which can increase the probability of early diagnosis of glaucoma. Besides the traditional methods, which depend on stratification results, this paper introduces a new method based on an end-to-end deep learning model to detect the BMO. Our model is composed of three parts. The first part is a CNN based retinal feature extraction network. It extracts feature map for both Optic Nerve Head (ONH) proposal and BMO detection. The second part is an ONH proposal network to detect region of interest (ROI) containing BMO. The third part is using the feature map from ONH proposal network to regress the location of BMO. The model has shown a clear precedence over other methods in terms of accuracy. Satisfactory results have been obtained when compared with clinical results.
KEYWORDS: Optical coherence tomography, Image segmentation, Medical imaging, Convolutional neural networks, Data modeling, Image processing algorithms and systems
Automated and quantitative analysis of the retinal lesions region is very needed in clinical practice. In this paper, we have proposed a method which effectively combines deep learning and improved distance regularized level set evolution (DRLSE) for automatically detecting and segmenting multiple retinal lesions in OCT volumes. The proposed method can segment five different retinal lesions: pigment epithelium detachment (PED), sub-retinal fluid (SRF), drusen, choroidal neovascularization (CNV), macular holes (MH). We tested 500 B-scans from 15 3D OCT volumes. The experimental results have validated the effectiveness and efficiency of the proposed method. The quantitative indices of average precision (AP), area under the curve (AUC) at intersection-over-union (IoU) that is equal to 0.50 : 0.05 : 0.95 and dice similarity coefficient (DICE) in average of 93.2%, 90.6% and 90.3% can be achieved, respectively.
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