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Fundus images of poor quality may seriously influence clinic judgments. Existing fundus image quality enhancement (FIQE) approaches mainly make use of general image features but no prior domain knowledge. In this paper, we proposed and validated an efficient FIQE method with a prior constraint, named Efficient Prior Contrastive unpaired Generative Adversarial Network (EPC-GAN). Inspired by the contrastive unpaired translation framework, we emphasized local features during the enhancing process via contrastive patchwise samples. Moreover, to utilize high-level features in the fundus domain (such as vessels, optic disc/cup, and even lesions), we designed a fundus prior loss to avoid information modification and over-enhancement. Besides, we presented an efficient network architecture to overcome the high consumption in terms of both time and GPU-memory. Through both qualitative and quantitative experiments on a public dataset EyeQ, we demonstrated the superior performance of our proposed method.

Precise quantification of the corneal nerve plexus morphology is of great importance in diagnosing peripheral diabetic neuropathy and assessing the progression of various eye-related systemic diseases, wherein segmentation of corneal nerves is an essential component. In this paper, we proposed and validated a novel pipeline for corneal nerve segmentation, comprising corneal confocal microscopy (CCM) image synthesis, image quality enhancement and nerve segmentation. Our goal was to address three major problems existing in most CCM datasets, namely inaccurate annotations, non-uniform illumination and contrast variations. In our synthesis and enhancement steps, we employed multilayer and patchwise contrastive learning based Generative Adversarial Network (GAN) frameworks, which took full advantage of multi-scale local features. Through both qualitative and quantitative experiments on two publicly available CCM datasets, our pipeline has achieved overwhelming enhancement performance compared to several state-of-the-art methods. Moreover, the segmentation results showed that models trained on our synthetic images performed much better than those trained on a real CCM dataset, which clearly identified the effectiveness of our synthesis method. Overall, our proposed pipeline can achieve satisfactory segmentation performance for poor-quality CCM images without using any manual labels and can effectively enhance those images.

Retinal vascular morphometry is an important biomarker of eye-related cardiovascular diseases such as diabetes and hypertension. And retinal vessel segmentation is a fundamental step in fundus image analyses and diagnoses. In recent years, deep learning based networks have achieved superior performance in medical image segmentation. However, for fine vessels or terminal branches, most existing methods tend to miss or under-segment those structures, inducing isolated breakpoints. In this paper, we proposed Bi-Directional ConvLSTM U-Net with Generative Adversarial Training (BLU-GAN), a novel deep learning model based on U-Net that generates precise predictions of retinal vessels combined with generative adversarial training. Bi-directional ConvLSTM, which can better integrate features from different scales through a coarse-to-fine memory mechanism, is employed to non-linearly combine feature maps extracted from encoding path layers and the previous decoding up-convolutional layers and to replace the simple skip-connection used in the original U-Net. Moreover, we use densely connected convolutions in certain layers to strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. Through extensive experiments, BLU-GAN has shown leading performance among the state-of-the-art methods on the DRIVE, STARE, CHASE_DB1 datasets for retinal vessel segmentation.

Manually annotating medical images is extremely expensive, especially for large-scale datasets. Self-supervised contrastive learning has been explored to learn feature representations from unlabeled images. However, unlike natural images, the application of contrastive learning to medical images is relatively limited. In this work, we propose a self-supervised framework, namely lesion-based contrastive learning for automated diabetic retinopathy (DR) grading. Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading. We also investigate different data augmentation operations in defining our contrastive prediction task. Extensive experiments are conducted on the publicly-accessible dataset EyePACS, demonstrating that our proposed framework performs outstandingly on DR grading in terms of both linear evaluation and transfer capacity evaluation.

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal layers. Clinical researches suggest that the morphology and contour irregularity of FAZ are important biomarkers of various ocular pathologies. Therefore, precise segmentation of FAZ has great clinical interest. Also, there is no existing research reporting that FAZ features can improve the performance of deep diagnostic classification networks. In this paper, we propose a novel multi-level boundary shape and distance aware joint learning framework, named BSDA-Net, for FAZ segmentation and diagnostic classification from OCTA images. Two auxiliary branches, namely boundary heatmap regression and signed distance map reconstruction branches, are constructed in addition to the segmentation branch to improve the segmentation performance, resulting in more accurate FAZ contours and fewer outliers. Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier. Through extensive experiments, the proposed BSDA-Net is found to yield state-of-the-art segmentation and classification results on the OCTA-500, OCTAGON, and FAZID datasets.

Optical coherence tomography angiography (OCTA) is a recent advance in ophthalmic imaging, which provides detailed visualization of two important anatomical landmarks, namely foveal avascular zone (FAZ) and retinal vessels (RV). Studies have shown that both FAZ and RV play significant roles in the diagnoses of various eye-related diseases. Therefore, accurate segmentation of FAZ and RV from OCTA images is highly in need. However, due to complicated microstructures and inhomogeneous image quality, there is still room for improvement in existing methods. In this paper, we propose a novel and efficient deep learning framework containing two subnetworks for simultaneously segmenting FAZ and RV from en-face OCTA images, named FARGO. For FAZ, we use RV segmentation as an auxiliary task, which may provide supplementary information especially for low-contrast and low-quality OCTA images. A ResNeSt based encoder with split attention and ImageNet pretraining is employed for FAZ segmentation. For RV, we introduce a coarse-to-fine cascaded network composed of a main segmentation model and several small ones for progressive refining. Spatial attention and channel attention modules are utilized for adaptively integrating local features with global dependencies. Through extensive experiments, FARGO is found to yield outstanding segmentation results for both FAZ and RV on the OCTA-500 dataset, performing even better than methods that utilize 3D OCTA volume as an extra input.

In this paper, we proposed an automatic pipeline for classifying three types of corneal ulcer patterns (point like, mixed, flaky). We first made use of H-channel related information in HSV space within cornea to perform corneal ulcers’ coarse segmentation. This result was then sequentially classified using two binary classification models: 1) Point-like ulcer against mixed and flaky ulcer, and 2) mixed ulcer against flaky ulcer. The proposed pipeline was evaluated on 712 corneal ulcer images, with an accuracy of 0.858 and a kappa score of 0.796 having been obtained. We also investigated the performance of our proposed pipeline with different baseline network architectures, and identified Resnet101 to be the best-performing one.

In this paper, we proposed and validated a probability distribution guided network for segmenting optic disc (OD) and optic cup (OC) from fundus images. Uncertainty is inevitable in deep learning, as induced by different sensors, insufficient samples, and inaccurate labeling. Since the input data and the corresponding ground truth label may be inaccurate, they may actually follow some potential distribution. In this study, a variational autoencoder (VAE) based network was proposed to estimate the joint distribution of the input image and the corresponding segmentation (both the ground truth segmentation and the predicted segmentation), making the segmentation network learn not only pixel-wise information but also semantic probability distribution. Moreover, we designed a building block, namely the Dilated Inception Block (DIB), for a better generalization of the model and a more effective extraction of multi-scale features. The proposed method was compared to several existing state-of-the-art methods. Superior segmentation performance has been observed over two datasets (ORIGA and REFUGE), with the mean Dice overlap coefficients being 96.57% and 95.81% for OD and 88.46% and 88.91% for OC.

In this paper, we proposed and validated a novel and effective pipeline for automatically detecting hemorrhage from coarsely-annotated fundus images in diabetic retinopathy. The proposed framework consisted of three parts: image pre-processing, training data refining, and object detection using a convolutional neural network with label smoothing. Contrast limited adaptive histogram equalization and adaptive gamma correction with weighting distribution were adopted to improve image quality by enhancing image contrast and correcting image illumination. To refine coarsely-annotated training data, we designed a bounding box refining network (BBR-net) to provide more accurate bounding box annotations. Combined with label smoothing, RetinaNet was implemented to alleviate mislabeling issues and automatically detect hemorrhages. The proposed method was trained and evaluated on a publicly available IDRiD dataset and also one of our private datasets 11This dataset will be released soon.. Experimental results showed that our BBR-net could effectively refine manually-delineated coarse hemorrhage annotations, with the average IoU being 0.8715 when compared with well-annotated bounding boxes. The proposed hemorrhage detection pipeline was compared to pure RetinaNet and superior performance was observed.

Monitoring the condition of retinal vascular network based on a fundus image plays a vital role in the diagnosis of certain ophthalmologic and cardiovascular diseases, for which a prerequisite is to segment out the retinal vessels. The relatively low contrast of retinal vessels and the presence of various types of lesions such as hemorrhages and exudate nevertheless make this task challenging. In this paper, we proposed and validated a novel retinal vessel segmentation method utilizing Separable Spatial and Channel Flow and Densely Adjacent Vessel Prediction to capture maximum spatial correlations between vessels. Image pre-processing was conducted to enhance the retinal vessel contrast. Geometric transformations and overlapped patches were used at both training and prediction stages to effectively utilize the information learned at the training stage and refine the segmentation. Publicly available datasets including DRIVE and CHASE_DB1 were used to evaluate the proposed approach both quantitatively and qualitatively. The proposed method was found to exhibit superior performance, with the average areas under the ROC curve being 0.9826 and 0.9865 and the average accuracies being 0.9579 and 0.9664 for the aforementioned two datasets, which outperforms existing state-of-the-art results.