The assistance of deep learning techniques for clinic doctors in disease analysis, diagnosis and treatment is becoming popular and popular. In this paper, we propose a U-shape architecture based Group Attention network (named as GANet) for symptom segmentation in fundus images with diabetic retinopathy, in which Channel Group Attention(CGA) module and Spatial Group Attention Upsampling (SGAU) module are designed. The CGA module can adaptively allocate resources based on the importance of the feature channels, which can enhance the flexibility of the network to handle different types of information. The original U-Net directly merges the high-level features and low-level features in decoder stage for semantic segmentation, and achieves good results. To increase the nonlinearity of the U-shape network and pay more attention to the lesion area, we propose a Spatial Group Attention Upsampling (SGAU) module. In summary, our main contributions include two aspects: (1) Based on the U-shape network, the CGA module and SGAU module are designed and applied, which can adaptively allocate the weight of channels and pay more attention to the lesion area, respectively. (2) Compared with the original U-Net, the Dice coefficients of the proposed network improves by nearly 2.96% for hard exudates segmentation and 2.89% for hemorrhage segmentation, respectively.
KEYWORDS: Optical coherence tomography, Image segmentation, Global system for mobile communications, Retina, Eye, Image fusion, Visualization, Convolution, Ophthalmology, Network architectures
The choroid is an important structure of the eye and choroid thickness distribution estimated from optical coherence tomography (OCT) images plays a vital role in analysis of many retinal diseases. This paper proposes a novel group-wise attention fusion network (referred to as GAF-Net) to segment the choroid layer, which can effectively work for both normal and pathological myopia retina. Currently, most networks perform unified processing of all feature maps in the same layer, which leads to not satisfactory choroid segmentation results. In order to improve this , GAF-Net proposes a group-wise channel module (GCM) and a group-wise spatial module (GSM) to fuse group-wise information. The GCM uses channel information to guide the fusion of group-wise context information, while the GSM uses spatial information to guide the fusion of group-wise context information. Furthermore, we adopt a joint loss to solve the problem of data imbalance and the uneven choroid target area. Experimental evaluations on a dataset composed of 1650 clinically obtained B-scans show that the proposed GAF-Net can achieve a Dice similarity coefficient of 95.21±0.73%.
Diabetic retinopathy (DR), a highly specific vascular complication caused by diabetes, has been found a major cause of blindness in the world. Early screening of DR is crucial for prevention of vision loss. Hard exudates (HEs) is one of the main manifestations of DR, which is characterized by hyper-reflective foci (HF) in retinal optical coherence tomography(OCT) images. In this paper, a fully automated method based on U-shape network is proposed to segment HF in retinal OCT images. Compared with the original U-Net, there are two main improvements in the proposed network:(1) The ordinary 3×3 convolution is replaced by multi-scale convolution based on dilated convolution, which can achieve adaptive receptive fields of the images. (2) In order to ignore irrelevant information and focus on key information in the channels, the channel attention module is embedded in the model. A dataset consisting of 112 2D OCT B-scan images was used to evaluate the proposed U-shape network for HF segmentation with 4-fold cross validation. The mean and standard deviation of Dice similarity coefficient, recall and precision are 73.26±2.03%, 75.71±1.98% and 74.28± 2.67%, respectively. The experimental results show the effectiveness of the proposed method.
Corneal confocal microscopy (CCM) is a new technique offering non-invasive and fast imaging useful for diagnosing and analyzing corneal diseases. The morphology of corneal nerve fibres can be clearly observed from CCM images. Segmentation and quantification of nerve fibres is important for analyzing corneal diseases such as diabetic peripheral neuropathy (DPN). In this paper, we propose an automated deep learning based method for corneal nerve fibre segmentation in CCM images. The main contributions of this paper are: (1)We add multi-scale split and concatenate (MSC) blocks to the decoding part of the four layer U-Net architecture. (2) A new loss function is applied that combining the Dice loss with the fibre length difference between the ground truth and the prediction. The method was tested on a dataset containing 90 CCM images from 4 normal eyes and 4 eyes with corneal diseases. The Dice coefficient of our approach can reach 87.96%, improves 1.6% compared with the baseline, and outperforms some existing deep networks for segmentation.
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.
In order to make further and more accurate automatic analysis and processing of optical coherence tomography (OCT) images, such as layer segmentation, disease region segmentation, registration, etc, it is necessary to screen OCT images first. In this paper, we propose an efficient multi-class 3D retinal OCT image classification network named as VinceptionC3D. VinceptionC3D is a 3D convolutional neural network which is improved from basic C3D by adding improved 3D inception modules. Our main contributions are: (1) Demonstrate that a fine-tuned C3D which is pretrained on nature action video datasets can be applied for the classification of 3D retinal OCT images; (2) Improve the network by employing 3D inception module which can capture multi-scale features. The proposed method is trained and tested on 873 3D OCT images with 6 classes. The average accuracy of the C3D with random initialization weights, the C3D with pre-trained weights, and the proposed VinceptionC3D with pre-trained weights are 89.35%, 92.09% and 94.04%, respectively. The result shows that the proposed VinceptionC3D is effective for the 6-class 3D retinal OCT image classification.
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