Diabetes affects one in eleven adults. Diabetic retinopathy is a microvascular complication of diabetes and the leading cause of blindness in the working-age population. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper proposes an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network for detection of microaneurysms is proposed. Compared to other methods that require five processing stages, it requires only two. Furthermore, a novel network fine-tuning scheme called Interleaved Freezing is presented. This procedure significantly reduces the amount of time needed to re-train a network and produces competitive results. The proposed method was evaluated using publicly available and widely used datasets: E-Ophtha and ROC. It outperforms the state-of-the-art methods in terms of free-response receiver operatic characteristic (FROC) metric. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications.
Diabetic retinopathy is an eye disease associated with diabetes mellitus and also it is the leading cause of preventable blindness in working-age population. Early detection and treatment of DR is essential to prevent vision loss. Exudates are one of the earliest signs of diabetic retinopathy. This paper proposes an automatic method for the detection and segmentation of exudates in fundus photographies. A novel fully convolutional neural network architecture with Inception modules is proposed. Compared to other methods it does not require the removal of other anatomical structures. Furthermore, a transfer learning approach is applied between small datasets of different modalities from the same domain. To the best of authors’ knowledge, it is the first time that such approach has been used in the exudate segmentation domain. The proposed method was evaluated using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms of sensitivity and specificity metrics. The proposed algorithm accomplished better results using a diseased/not diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications.
Similarity learning is one of the most fundamental tasks in image analysis. The ability to extract similar images in the medical domain as part of content-based image retrieval (CBIR) systems has been researched for many years. The vast majority of methods used in CBIR systems are based on hand-crafted feature descriptors. The approximation of a similarity mapping for medical images is difficult due to the big variety of pixel-level structures of interest. In fundus photography (FP) analysis, a subtle difference in e.g. lesions and vessels shape and size can result in a different diagnosis. In this work, we demonstrated how to learn a similarity function for image patches derived directly from FP image data without the need of manually designed feature descriptors. We used a convolutional neural network (CNN) with a novel architecture adapted for similarity learning to accomplish this task. Furthermore, we explored and studied multiple CNN architectures. We show that our method can approximate the similarity between FP patches more efficiently and accurately than the state-of- the-art feature descriptors, including SIFT and SURF using a publicly available dataset. Finally, we observe that our approach, which is purely data-driven, learns that features such as vessels calibre and orientation are important discriminative factors, which resembles the way how humans reason about similarity. To the best of authors knowledge, this is the first attempt to approximate a visual similarity mapping in FP.