Microaneurysm detection using fully convolutional neural networks. (May 2018) - Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew
Highlights: An automatic method for detecting microaneurysms in fundus images is proposed. It uses a fully convolutional neural network with batch normalization and Dice loss. It requires only two processing stages. Shows how to transfer knowledge between datasets in microaneurysm domain. Produces better results than state-of-the-art methods. Abstract: Backround and Objectives: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. Methods: A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. Results: The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Conclusions: Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications.