Deep Retinal Image Segmentation With Regularization Under Geometric Priors

Abstract

Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. This problem faces several challenges including low contrast, variable vessel size and thickness, and presence of interfering pathology such as micro-aneurysms and hemorrhages. Early approaches addressing this problem employed hand-crafted filters to capture vessel structures, accompanied by morphological post-processing. More recently, deep learning techniques have been employed with significantly enhanced segmentation accuracy. We propose a novel domain enriched deep network that consists of two components: 1) a representation network that learns geometric features specific to retinal images, and 2) a custom designed computationally efficient residual task network that utilizes the features obtained from the representation layer to perform pixel-level segmentation. The representation and task networks are jointly learned for any given training set. To obtain physically meaningful and practically effective representation filters, we propose two new constraints that are inspired by expected prior structure on these filters: 1) orientation constraint that promotes geometric diversity of curvilinear features, and 2) a data adaptive noise regularizer that penalizes false positives. Multi-scale extensions are developed to enable accurate detection of thin vessels. Experiments performed on three challenging benchmark databases under a variety of training scenarios show that the proposed prior guided deep network outperforms state of the art alternatives as measured by common evaluation metrics, while being more economical in network size and inference time.

Influence of background preprocessing on the performance of deep learning retinal vessel detection

“…Cherukuri et al 23 exploited knowledge about the structure of vessels by embedding two geometrical priors into the loss function that was used to jointly train two networks: a representation layer and a residual task network. These two priors had the effect of: i) encouraging diversity in vessel orientation, and ii) adaptively penalizing noise to suppress false positives.…”

Retinal Vessel Segmentation Using Deep Learning: A Review

“…Cherukuri, et al [134] proposed a domain-enriched network that was composed of two parts: a representation network to geometric features from fundus images and a residual task network to make a pixel-level prediction using the obtained features. Their method obtained a good performance but there are still non-vessel pixels identified as vessel pixels.…”

Retinal Image Classification by Self-supervised Fuzzy Clustering Network

“…In previous studies, diabetic retinopathy was automatically detected by various models, which are principally separated into two categories (conventional machine learning approaches [20], [23], and deep learning methodologies [11], [40]). For the first category, several traditional models are employed into retinal image analysis.…”