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The automatic exudate segmentation in colour retinal fundus images is an important task in computer aided diagnosis and screening systems for diabetic retinopathy. In this paper, we present a location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images, which includes three stages: anatomic structure removal, exudate location and exudate segmentation. In anatomic structure removal stage, matched filters based main vessels segmentation method and a saliency based optic disk segmentation method are proposed. The main vessel and optic disk are then removed to eliminate the adverse affects that they bring to the second stage. In the location stage, we learn a random forest classifier to classify patches into two classes: exudate patches and exudate-free patches, in which the histograms of completed local binary patterns are extracted to describe the texture structures of the patches. Finally, the local variance, the size prior about the exudate regions and the local contrast prior are used to segment the exudate regions out from patches which are classified as exudate patches in the location stage. We evaluate our method both at exudate-level and image-level. For exudate-level evaluation, we test our method on e-ophtha EX dataset, which provides pixel level annotation from the specialists. The experimental results show that our method achieves 76% in sensitivity and 75% in positive prediction value (PPV), which both outperform the state of the art methods significantly. For image-level evaluation, we test our method on DiaRetDB1, and achieve competitive performance compared to the state of the art methods.

When dealing with the optic disc and cup in the optical nerve head images, their joint segmentation confronts two critical problems. One is that the spatial layout of the vessels in the optic nerve head images is variant. The other is that the landmarks for the optic cup boundaries are spatially sparse and at small spatial scale. To solve these two problems, we propose a spatial-aware joint segmentation method by explicitly considering the spatial locations of the pixels and learning the multi-scale spatially dense features. We formulate the joint segmentation task from a probabilistic perspective, and derive a spatial-aware maximum conditional probability framework and the corresponding error function. Accordingly, we provide an end-to-end solution by designing a spatial-aware neural network. It consists of three modules: the atrous CNN module to extract the spatially dense features, the pyramid filtering module to produce the spatial-aware multi-scale features, and the spatial-aware segmentation module to predict the labels of pixels. We validate the state-of-the-art performances of our spatial-aware segmentation method on two public datasets, i.e., ORIGA and DRISHTI. Based on the segmentation masks, we quantify the cup-to-disk values and apply them to the glaucoma screening. High correlation between the cup-to-disk values and the risks of the glaucoma is validated on the dataset ORIGA.

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Objective. This study is aimed at investigating differences in local brain activity and functional connectivity (FC) between children with unilateral amblyopia and healthy controls (HCs) by using resting state functional magnetic resonance imaging (rs-fMRI). Methods. Local activity and FC analysis methods were used to explore the altered spontaneous brain activity of children with unilateral amblyopia. Local brain function analysis methods included the amplitude of low-frequency fluctuation (ALFF). FC analysis methods consisted of the FC between the primary visual cortex (PVC-FC) and other brain regions and the FC network between regions of interest (ROIs-FC) selected by independent component analysis. Results. The ALFF in the bilateral frontal, temporal, and occipital lobes in the amblyopia group was lower than that in the HCs. The weakened PVC-FC was mainly concentrated in the frontal lobe and the angular gyrus. The ROIs-FC between the default mode network, salience network, and primary visual cortex network (PVCN) were significantly reduced, whereas the ROIs-FC between the PVCN and the high-level visual cortex network were significantly increased in amblyopia. Conclusions. Unilateral amblyopia may reduce local brain activity and FC in the dorsal and ventral visual pathways and affect the top-down attentional control. Amblyopia may also alter FC between brain functional networks. These findings may help understand the pathological mechanisms of children with amblyopia.

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