Exudate segmentation using fully convolutional neural networks and inception modules

Piotr Chudzik; Somshubra Majumdar; Francesco Caliva; Bashir Al-Diri; Andrew Hunter

doi:10.1117/12.2293549

2 March 2018 Exudate segmentation using fully convolutional neural networks and inception modules

Piotr Chudzik, Somshubra Majumdar, Francesco Caliva, Bashir Al-Diri, Andrew Hunter

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 1057430 (2018) https://doi.org/10.1117/12.2293549
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

Citation Download Citation

Piotr Chudzik, Somshubra Majumdar, Francesco Caliva, Bashir Al-Diri, and Andrew Hunter "Exudate segmentation using fully convolutional neural networks and inception modules", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057430 (2 March 2018); https://doi.org/10.1117/12.2293549

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