Diabetic retinopathy (DR) is the most common chronic complication of diabetes and the first blinding eye disease in the working population. Hard exudates (HE) is an obvious symptom of diabetic retinopathy, which has high reflectivity to light and appears as hyperreflective foci (HRF) in optical coherence tomography (OCT) images. Based on the research and improvement of U-Net, this paper proposes a selfadaptive network (SANet) for HRF segmentation. There are two main improvements in the proposed SANet: (1) In order to simplify the learning process and enhance the gradient propagation, the ordinary convolution block in the encoder structure is replaced by a dual residual module (DRM). (2) The novel self-adaptive module (SAM) is embedded in the deep layer of the model, which enables the network to integrate local features and global dependencies adaptively, and makes it adapt to the irregular shape of HRF. The dataset consists of 112 2D OCT B-scan images, which were verified by four-fold cross validation. The mean and standard deviation of Dice similarity coefficient, Jaccard index, Sensitivity and Precision are 73.69±0.72%, 59.17±1.00%, 74.57±1.16% and 75.54±1.35%, respectively. The experimental results show that the proposed method can segment HRF successfully and the performance is better than the original U-Net.
Dermoscopy is a non-invasive dermatology imaging and widely used in dermatology clinic. In order to screen and detect melanoma automatically, skin lesion segmentation in dermoscopy images is of great significance. In this paper, we propose an adaptive scale network (ASNet) for skin lesion segmentation in dermoscopy images. A ResNet34 with pretrained weights is applied as the encoder to extract more representative features. A novel adaptive scale module is designed and inserted into the top of the encoder path to dynamically fuse multi-scale information, which can self-learn based on spatial attention mechanism. Our proposed method is 5-fold cross-validated on a public dataset from Challenge Lesion Boundary Segmentation in ISIC-2018, which includes 2594 images from different types of skin lesion with different resolutions. The Jaccard coefficient, Dice coefficient and Accuracy are 82.15±0.328%, 88.880.390% and 96.00±0.228%, respectively. Experimental results show the effectiveness of the proposed ASNet.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.