Medical Image Segmentation¶
Microaneurysm detection using fully convolutional neural networks¶
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.
Exudate segmentation using fully convolutional neural networks and inception modules¶
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.
Microaneurysm detection using deep learning and interleaved freezing¶
Abstract
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.