Songtao Yuan

Automated choroid segmentation based on gradual intensity distance in HD-OCT images.

The choroid is an important structure of the eye and plays a vital role in the pathology of retinal diseases. This paper presents an automated choroid segmentation method for high-definition optical coherence tomography (HD-OCT) images, including Bruchs membrane (BM) segmentation and choroidal-scleral interface (CSI) segmentation. An improved retinal nerve fiber layer (RNFL) complex removal algorithm is presented to segment BM by considering the structure characteristics of retinal layers. By analyzing the characteristics of CSI boundaries, we present a novel algorithm to generate a gradual intensity distance image. Then an improved 2-D graph search method with curve smooth constraints is used to obtain the CSI segmentation. Experimental results with 212 HD-OCT images from 110 eyes in 66 patients demonstrate that the proposed method can achieve high segmentation accuracy. The mean choroid thickness difference and overlap ratio between our proposed method and outlines drawn by experts was 6.72µm and 85.04%, respectively.

Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images

The segmentation of the fluid-associated region in the retina plays an important role in the treatment of retinal diseases, such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). The existing methods for the detection of the fluid region generally suffer from the limitation of the segmentation accuracy and the expensive time costs. To overcome these problems, in this paper, we propose an interactive segmentation method for the fluid-associated region in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) retinal imaging, in which only a few seeds in one SD-OCT slice are needed, after which the algorithm finishes the segmentation automatically. To improve the segmentation accuracy, the higher-order constraint is introduced into the conventional Markov random field (MRF) framework to impose the superpixel consistency. To maintain temporal coherence of the 3-D SD-OCT slices, the labeling information is propagated slice by slice via a proposed motion-estimation-based algorithm. The proposed higher-order-based energy function can be efficiently solved by the max flow algorithm on a specified graph with several auxiliary nodes. Experiments on 28 SD-OCT cubes demonstrate the competitiveness of the proposed method compared with the state-of-the-art methods.

Choroidal vasculature characteristics based choroid segmentation for enhanced depth imaging optical coherence tomography images

PURPOSEnIn clinical research, it is important to measure choroidal thickness when eyes are affected by various diseases. The main purpose is to automatically segment choroid for enhanced depth imaging optical coherence tomography (EDI-OCT) images with five B-scans averaging.nnnMETHODSnThe authors present an automated choroid segmentation method based on choroidal vasculature characteristics for EDI-OCT images with five B-scans averaging. By considering the large vascular of the Hallers layer neighbor with the choroid-sclera junction (CSJ), the authors measured the intensity ascending distance and a maximum intensity image in the axial direction from a smoothed and normalized EDI-OCT image. Then, based on generated choroidal vessel image, the authors constructed the CSJ cost and constrain the CSJ search neighborhood. Finally, graph search with smooth constraints was utilized to obtain the CSJ boundary.nnnRESULTSnExperimental results with 49 images from 10 eyes in 8 normal persons and 270 images from 57 eyes in 44 patients with several stages of diabetic retinopathy and age-related macular degeneration demonstrate that the proposed method can accurately segment the choroid of EDI-OCT images with five B-scans averaging. The mean choroid thickness difference and overlap ratio between the authors proposed method and manual segmentation drawn by experts were -11.43 μm and 86.29%, respectively.nnnCONCLUSIONSnGood performance was achieved for normal and pathologic eyes, which proves that the authors method is effective for the automated choroid segmentation of the EDI-OCT images with five B-scans averaging.

New Developments in the Classification, Pathogenesis, Risk Factors, Natural History, and Treatment of Branch Retinal Vein Occlusion

For years, branch retinal vein occlusion is still a controversial disease in many aspects. An increasing amount of data is available regarding classification, pathogenesis, risk factors, natural history, and therapy of branch retinal vein occlusion. Some of the conclusions may even change our impression of branch retinal vein occlusion. It will be beneficial for our doctors to get a deeper understanding of this disease and improve the treatment skills. The aims of this review is to collect the information above and report new ideas especially from the past a few years.

Multimodality analysis of Hyper-reflective Foci and Hard Exudates in Patients with Diabetic Retinopathy

To investigate the correlations between hyper-reflective foci and hard exudates in patients with non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) by spectral-domain optical coherence tomography (SD OCT) images. Hyper-reflective foci in retinal SD OCT images were automatically detected by the developed algorithm. Then, the cropped CFP images generated by the semi-automatic registration method were automatically segmented for the hard exudates and corrected by the experienced clinical ophthalmologist. Finally, a set of 5 quantitative imaging features were automatically extracted from SD OCT images, which were used for investigating the correlations of hyper-reflective foci and hard exudates and predicting the severity of diabetic retinopathy. Experimental results demonstrated the positive correlations in area and amount between hard exudates and hyper-reflective foci at different stages of diabetic retinopathy, with statistical significance (all pu2009<u20090.05). In addition, the area and amount can be taken as potential discriminant indicators of the severity of diabetic retinopathy.

Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy

Assessment of serous retinal detachment plays an important role in the diagnosis of central serous chorioretinopathy (CSC). In this paper, we propose an automatic, three-dimensional segmentation method to detect both neurosensory retinal detachment (NRD) and pigment epithelial detachment (PED) in spectral domain optical coherence tomography (SD-OCT) images. The proposed method involves constructing a probability map from training samples using random forest classification. The probability map is constructed from a linear combination of structural texture, intensity, and layer thickness information. Then, a continuous max flow optimization algorithm is applied to the probability map to segment the retinal detachment-associated fluid regions. Experimental results from 37 retinal SD-OCT volumes from cases of CSC demonstrate the proposed method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), positive predicative value (PPV), and dice similarity coefficient (DSC) of 92.1%, 0.53%, 94.7%, and 93.3%, respectively, for NRD segmentation and 92.5%, 0.14%, 80.9%, and 84.6%, respectively, for PED segmentation. The proposed method can be an automatic tool to evaluate serous retinal detachment and has the potential to improve the clinical evaluation of CSC.

Beyond Retinal Layers: A Large Blob Detection for Subretinal Fluid Segmentation in SD-OCT Images

Purpose: To automatically segment neurosensory retinal detachment (NRD)-associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images by constructing a Hessian-based Aggregate generalized Laplacian of Gaussian algorithm without the use of retinal layer segmentation. Methods: The B-scan is first filtered into small blob candidate regions based on local convexity by aggregating the log-scale-normalized convolution responses of each individual gLoG filter. Two Hessian-based regional features are extracted based on the aggregate response map. Pooling with regional intensity, the feature vectors are fed into an unsupervised clustering algorithm. By voting the blob candidates into the superpixels, the initial subretinal fluid regions are obtained. Finally, an active contour with narrowband implementation is utilized to obtain integrated segmentations. Results: The testing data set with 23 longitudinal SD-OCT cube scans from 12 eyes of 12 patients are used to evaluate the proposed algorithm. Comparing with two independent experts’ manual segmentations, our algorithm obtained a mean true positive volume fraction 95.15%, positive predicative value 93.65% and dice similarity coefficient 94.35%, respectively. Conclusions: Without retinal layer segmentation, the proposed algorithm can produce higher segmentation accuracy comparing with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable subretinal fluid segmentations for NRD from SD-OCT images.

Automated Choroidal Neovascularization Detection for Time Series SD-OCT Images

Choroidal neovascularization (CNV), caused by new blood vessels in the choroid growing through the Bruch’s membrane, is an important manifestation of terminal age-related macular degeneration (AMD). Automated CNV detection in three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) images is still a huge challenge. This paper presents an automated CNV detection method based on object tracking strategy for time series SD-OCT volumetric images. In our proposed scheme, experts only need to manually calibrate CNV lesion area for the first moment of each patient, and then the CNV of the following moments will be automatically detected. In order to fully represent space consistency of CNV, a 3D-histogram of oriented gradient (3D-HOG) feature is constructed for the generation of random forest model. Finally, the similarity between training and testing samples is measured for model updating. The experiments on 258 SD-OCT cubes from 12 eyes in 12 patients with CNV demonstrate that our results have a high correlation with the manual segmentations. The average of correlation coefficients and overlap ratio for CNV projection area are 0.907 and 83.96%, respectively.

Evaluation of day care versus inpatient cataract surgery performed at a Jiangsu public Tertiary A hospital

BackgroundHigh cataract incidence and low cataract surgical rate are serious public health problems in China, despite the fact that efficient day care cataract surgery has been implemented in some public Tertiary A hospitals in China. In this study, we compared not only clinical outcomes, hospitalization time and total costs but also payment manners between day care and inpatient proceduresxa0for cataract surgery in a Jiangsu public Tertiary A hospital to put forward several instructional suggestions for the improvement of government medical policies.MethodsIn total, 4151xa0day care cases and 2509 inpatient cases underwent the same cataract surgery in the day care ward and ordinary ward respectively, and were defined as two groups. General information, complications, postoperative best corrected visual acuity (BCVA), hospitalization time, total costs and especially payment method were analyzed to compare day care versus inpatient.ResultsThe general data display no significant differences (Pu2009>u20090.05), and no significant difference between complications and postoperative BCVA were observed between the two groups (Pu2009>u20090.05). The period of stay in hospital was significantly different (Pu2009<u20090.001). The total costs were lower for day care than for inpatients (Pu2009<u20090.001). To avoid sampling error, we analyzed the data of payment manner for each patient among this period. Day care patients tended to pay for the procedure using the Urban Employees Basic Medical Insurance (UEBMI) method, while inpatients tended to use the Out-of-Pocket Medical Treatment (OMT) payment method (Pu2009<u20090.001).ConclusionDay surgery of cataract is more cost-effective and efficient than inpatient surgery with equivalent clinical outcomes. As an efficient therapeutic regimen, day care surgery should be further promoted and supported by the government policies.

Registration of OCT Fundus Images with Color Fundus Images Based on Invariant Features

Disease diagnosis and treatment are often supported by multiple images acquired from the same patient. Multimodal retinal fundus image registration techniques are fundamental to integrate the information gained from several fundus images for a comprehensive understanding. In this paper, we proposed an algorithm for registration of OCT fundus images (OFIs) with color fundus photographs (CFPs) based on invariant features. The local similarity function is defined based on the blood vessel ridges of retinal fundus images. According to the local maximum similarity function, we can extract effective image blocks and then acquire the feature matching points. We can finally achieve the registration by utilizing the quadratic surface model to calculate the transformation matrix parameters. The proposed algorithm was tested on a sample set containing 3 normal eyes and 18 eyes with age-related macular degeneration. The experiment demonstrates that the proposed method has high accuracy (root mean square error is 111.06 μm) in different qualities for both of color fundus images and OCT fundus images.