Sijie Niu

Robust noise region-based active contour model via local similarity factor for image segmentation

Abstract Image segmentation using a region-based active contour model could present difficulties when its noise distribution is unknown. To overcome this problem, this paper proposes a novel region-based model for the segmentation of objects or structures in images by introducing a local similarity factor, which relies on the local spatial distance within a local window and local intensity difference to improve the segmentation results. By using this local similarity factor, the proposed method can accurately extract the object boundary while guaranteeing certain noise robustness. Furthermore, the proposed algorithm completely avoids the pre-processing steps typical of region-based contour model segmentation, resulting in a higher preservation of image details. Experiments performed on synthetic images and real word images demonstrate that the proposed algorithm, as compared with the state-of-art algorithms, is more efficient and robust to higher noise level manifestations in the images.

Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint

Automatic segmentation of retinal layers in spectral domain optical coherence tomography (SD-OCT) images plays a vital role in the quantitative assessment of retinal disease, because it provides detailed information which is hard to process manually. A number of algorithms to automatically segment retinal layers have been developed; however, accurate edge detection is challenging. We developed an automatic algorithm for segmenting retinal layers based on dual-gradient and spatial correlation smoothness constraint. The proposed algorithm utilizes a customized edge flow to produce the edge map and a convolution operator to obtain local gradient map in the axial direction. A valid search region is then defined to identify layer boundaries. Finally, a spatial correlation smoothness constraint is applied to remove anomalous points at the layer boundaries. Our approach was tested on two datasets including 10 cubes from 10 healthy eyes and 15 cubes from 6 patients with age-related macular degeneration. A quantitative evaluation of our method was performed on more than 600 images from cubes obtained in five healthy eyes. Experimental results demonstrated that the proposed method can estimate six layer boundaries accurately. Mean absolute boundary positioning differences and mean absolute thickness differences (mean±SD) were 4.43±3.32 μm and 0.22±0.24 μm, respectively.

Fully Automated Prediction of Geographic Atrophy Growth Using Quantitative Spectral-Domain Optical Coherence Tomography Biomarkers

PURPOSE To develop a predictive model based on quantitative characteristics of geographic atrophy (GA) to estimate future potential regions of GA growth. DESIGN Progression study and predictive model. PARTICIPANTS One hundred eighteen spectral-domain (SD) optical coherence tomography (OCT) scans of 38 eyes in 29 patients. METHODS Imaging features of GA quantifying its extent and location, as well as characteristics at each topographic location related to individual retinal layer thickness and reflectivity, the presence of pathologic features (like reticular pseudodrusen or loss of photoreceptors), and other known risk factors of GA growth, were extracted automatically from 118 SD OCT scans of 38 eyes from 29 patients collected over a median follow-up of 2.25 years. We developed and evaluated a model to predict the magnitude and location of GA growth at given future times using the quantitative features as predictors in 3 possible scenarios. MAIN OUTCOME MEASURES Potential regions of GA growth. RESULTS In descending order of out-of-bag feature importance, the most predictive SD OCT biomarkers for predicting the future regions of GA growth were thickness loss of bands 11 through 14 (5.66), reflectivity of bands 11 and 12 (5.37), thickness of reticular pseudodrusen (5.01), thickness of bands 5 through 11 (4.82), reflectivity of bands 7 through 11 (4.78), GA projection image (4.73), increased minimum retinal intensity map (4.59), and GA eccentricity (4.49). The predicted GA regions in the 3 tested scenarios resulted in a Dice index mean ± standard deviation of 0.81±0.12, 0.84±0.10, and 0.87±0.06, respectively, when compared with the observed ground truth. Considering only the regions without evidence of GA at baseline, predicted regions of future GA growth showed relatively high Dice indices of 0.72±0.18, 0.74±0.17, and 0.72±0.22, respectively. Predictions and actual values of GA growth rate and future GA involvement in the central fovea showed high correlations. CONCLUSIONS Experimental results demonstrated the potential of our predictive model to predict future regions where GA is likely to grow and to identify the most discriminant early indicator (thickness loss of bands 11 through 14) of regions susceptible to GA growth.

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.

Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor.

Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals. Geographic atrophy (GA) is a phenotypic manifestation of the advanced stages of non-exudative AMD. Determination of GA extent in SD-OCT scans allows the quantification of GA-related features, such as radius or area, which could be of important value to monitor AMD progression and possibly identify regions of future GA involvement. The purpose of this work is to develop an automated algorithm to segment GA regions in SD-OCT images. An en face GA fundus image is generated by averaging the axial intensity within an automatically detected sub-volume of the three dimensional SD-OCT data, where an initial coarse GA region is estimated by an iterative threshold segmentation method and an intensity profile set, and subsequently refined by a region-based Chan-Vese model with a local similarity factor. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to quantitatively evaluate the automated segmentation algorithm. We compared results obtained by the proposed algorithm, manual segmentation by graders, a previously proposed method, and experimental commercial software. When compared to a manually determined gold standard, our algorithm presented a mean overlap ratio (OR) of 81.86% and 70% for the first and second data sets, respectively, while the previously proposed method OR was 72.60% and 65.88% for the first and second data sets, respectively, and the experimental commercial software OR was 62.40% for the second data set.

A false color fusion strategy for drusen and geographic atrophy visualization in optical coherence tomography images.

Purpose: To display drusen and geographic atrophy (GA) in a single projection image from three-dimensional spectral domain optical coherence tomography images based on a novel false color fusion strategy. Methods: We present a false color fusion strategy to combine drusen and GA projection images. The drusen projection image is generated with a restricted summed-voxel projection (axial sum of the reflectivity values in a spectral domain optical coherence tomography cube, limited to the region where drusen is present). The GA projection image is generated by incorporating two GA characteristics: bright choroid and thin retina pigment epithelium. The false color fusion method was evaluated in 82 three-dimensional optical coherence tomography data sets obtained from 7 patients, for which 2 readers independently identified drusen and GA as the gold standard. The mean drusen and GA overlap ratio was used as the metric to determine accuracy of visualization of the proposed method when compared with the conventional summed-voxel projection, (axial sum of the reflectivity values in the complete spectral domain optical coherence tomography cube) technique and color fundus photographs. Results: Comparative results demonstrate that the false color image is more effective in displaying drusen and GA than summed-voxel projection and CFP. The mean drusen/GA overlap ratios based on the conventional summed-voxel projection method, color fundus photographs, and the false color fusion method were 6.4%/100%, 64.1%/66.7%, and 85.6%/100%, respectively. Conclusion: The false color fusion method was more effective for simultaneous visualization of drusen and GA than the conventional summed-voxel projection method and color fundus photographs, and it seems promising as an alternative method for visualizing drusen and GA in the retinal fundus, which commonly occur together and can be confusing to differentiate without methods such as this proposed one.

Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images

Purpose To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation. Methods An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer. During the training phase, the labeled A-scans with 1024 features were directly fed into the network as the input layer to obtain the deep representations. Then a soft-max classifier was trained to determine the label of each individual pixel. Finally, a voting decision strategy was used to refine the segmentation results among 10 trained models. Results Two image data sets with GA were used to evaluate the model. For the first dataset, our algorithm obtained a mean overlap ratio (OR) 86.94% ± 8.75%, absolute area difference (AAD) 11.49% ± 11.50%, and correlation coefficients (CC) 0.9857; for the second dataset, the mean OR, AAD, and CC of the proposed method were 81.66% ± 10.93%, 8.30% ± 9.09%, and 0.9952, respectively. The proposed algorithm was capable of improving over 5% and 10% segmentation accuracy, respectively, when compared with several state-of-the-art algorithms on two data sets. Conclusions Without retinal layer segmentation, the proposed algorithm could produce higher segmentation accuracy and was more stable when compared with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable GA segmentations from SD-OCT images and be useful in the clinical diagnosis of advanced nonexudative AMD. Translational Relevance Based on the deep neural networks, this study presents an accurate GA segmentation method for SD-OCT images without using any retinal layer segmentation results, and may contribute to improved understanding of advanced nonexudative AMD.

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 p < 0.05). In addition, the area and amount can be taken as potential discriminant indicators of the severity of diabetic retinopathy.

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.