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1.
IEEE Trans Med Imaging ; 40(5): 1363-1376, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33507867

RESUMO

To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

2.
Med Image Anal ; 68: 101893, 2020 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-33260118

RESUMO

The automated prediction of geographic atrophy (GA) lesion growth can help ophthalmologists understand how the GA progresses, and assess the efficiency of current treatment and the prognosis of the disease. We developed an integrated time adaptive prediction model for identifying the location of future GA growth. The proposed model was comprised of bi-directional long short-term memory (BiLSTM) network-based prediction module and convolutional neural network (CNN)-based refinement module. Considering the discontinuity of time intervals among sequential follow-up visits, we integrated time factors into BiLSTM-based prediction module to control the time attribute expediently. Then, the results from prediction module were refined by a CNN-based strategy to obtain the final locations of future GA growth. The 10 scenarios were designed to evaluate the prediction accuracy of our proposed model. The 1-6th scenarios demonstrated the importance of the prior information similarity, the 7-8th scenarios verified the effect of time factors and refinement methods respectively and the 9th scenario compared the prediction results between those using a single follow-up visit for training and using 2 sequential follow-up visits for training. The 10th scenario showed the model generalization performance across regions. The average dice indexes (DI) of the predicted GA regions in the 1-6th scenarios are 0.86, 0.89, 0.89, 0.92 and 0.88, 0.90, respectively. By integrating time factors to the BiLSTM models, the prediction accuracy was improved by almost 10%. The CNN-based refinement strategy can remove the wrong GA regions effectively to preserve the actual GA regions and improve the prediction accuracy further. The prediction results based on 2 sequential follow-up visits showed higher correlations than that based on single follow-up visit. The proposed model presented a good generalization performance while training patients and testing patients were from different regions. Experimental results demonstrated the importance of prior information to the prediction accuracy. We demonstrate the feasibility of creating a model for disease prediction.

3.
IEEE J Biomed Health Inform ; 24(12): 3443-3455, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32750923

RESUMO

As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions affected by GA is a fundamental step for clinical diagnosis. In this paper, we present a novel weakly supervised model for GA segmentation in Spectral-Domain Optical Coherence Tomography (SD-OCT) images. A novel Multi-Scale Class Activation Map (MS-CAM) is proposed to highlight the discriminatory significance regions in localization and detail descriptions. To extract available multi-scale features, we design a Scaling and UpSampling (SUS) module to balance the information content between features of different scales. To capture more discriminative features, an Attentional Fully Connected (AFC) module is proposed by introducing the attention mechanism into the fully connected operations to enhance the significant informative features and suppress less useful ones. Based on the location cues, the final GA region prediction is obtained by the projection segmentation of MS-CAM. The experimental results on two independent datasets demonstrate that the proposed weakly supervised model outperforms the conventional GA segmentation methods and can produce similar or superior accuracy comparing with fully supervised approaches. The source code has been released and is available on GitHub: https://github.com/ jizexuan/Multi-Scale-Class-Activation-Map-Tensorflow.

4.
IEEE J Biomed Health Inform ; 24(11): 3236-3247, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32191901

RESUMO

Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images.

5.
Comput Methods Programs Biomed ; 182: 105101, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31600644

RESUMO

BACKGROUND AND OBJECTIVE: Accurate assessment of geographic atrophy (GA) is critical for diagnosis and therapy of non-exudative age-related macular degeneration (AMD). Herein, we propose a novel GA segmentation framework for spectral-domain optical coherence tomography (SD-OCT) images that employs synthesized fundus autofluorescence (FAF) images. METHODS: An en-face OCT image is created via the restricted sub-volume projection of three-dimensional OCT data. A GA region-aware conditional generative adversarial network is employed to generate a plausible FAF image from the en-face OCT image. The network balances the consistency between the entire synthesize FAF image and the lesion. We use a fully convolutional deep network architecture to segment the GA region using the multimodal images, where the features of the en-face OCT and synthesized FAF images are fused on the front-end of the network. RESULTS: Experimental results for 56 SD-OCT scans with GA indicate that our synthesis algorithm can generate high-quality synthesized FAF images and that the proposed segmentation network achieves a dice similarity coefficient, an overlap ratio, and an absolute area difference of 87.2%, 77.9%, and 11.0%, respectively. CONCLUSION: We report an automatic GA segmentation method utilizing synthesized FAF images. SIGNIFICANCE: Our method is effective for multimodal segmentation of the GA region and can improve AMD treatment.


Assuntos
Fundo de Olho , Atrofia Geográfica/diagnóstico por imagem , Imagem Óptica/métodos , Tomografia de Coerência Óptica/métodos , Automação , Humanos
6.
Biomed Opt Express ; 10(8): 3987-4002, 2019 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-31452990

RESUMO

As a function of the spatial position of the optical coherence tomography (OCT) image, retinal layer thickness is an important diagnostic indicator for many retinal diseases. Reliable segmentation of the retinal layer is necessary for extracting useful clinical information. However, manual segmentation of these layers is time-consuming and prone to bias. Furthermore, due to speckle noise, low image contrast, retinal detachment, and also irregular morphological features make the automatic segmentation task challenging. To alleviate these challenges, in this paper, we propose a new coarse-fine framework combining the full convolutional network (FCN) with a multiphase level set (named FCN-MLS) for automatic segmentation of nine boundaries in retinal spectral OCT images. In the coarse stage, FCN is used to learn the characteristics of specific retinal layer boundaries and achieve classification of four retinal layers. The boundaries are then extracted and the remaining boundaries are initialized based on a priori information about the thickness of the retinal layer. In order to prevent the overlapping of the segmentation interfaces, a regional restriction technique is used in the multi-phase level to evolve the boundaries to achieve fine nine retinal layers segmentation. Experimental results on 1280 B-scans show that the proposed method can segment nine retinal boundaries accurately. Compared with the manual delineation, the overall mean absolute boundary location difference and the overall mean absolute thickness difference were 5.88 ± 2.38µm and 5.81 ± 2.19µm, which showed a good consistency with manual segmentation by the physicians. Our experimental results also outperform state-of-the-art methods.

7.
Med Phys ; 46(10): 4502-4519, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31315159

RESUMO

PURPOSE: The purpose of this study was to automatically and accurately segment hyper-reflective foci (HRF) in spectral domain optical coherence tomography (SD-OCT) images with diabetic retinopathy (DR) using deep convolutional neural networks. METHODS: An automatic HRF segmentation model for SD-OCT images based on deep networks was constructed. The model segmented small lesions through pixel-wise predictions based on small image patches. We used an approach for discriminative features extraction for small patches by introducing small kernels and strides in convolutional and pooling layers, which was applied on the state-of-the-art deep classification networks (GoogLeNet and ResNet). The features extracted by the adapted deep networks were fed into a softmax layer to produce the probabilities of HRF. We trained different models on a dataset with 16 HRF eyes by using different sizes of patches, and then, we fused these models to generate optimal results. RESULTS: Experimental results on 18 eyes demonstrated that our method is effective for the HRF segmentation. The dice similarity coefficient (DSC) for the foci area in B-scan, projection images, and foci amount in B-scan images reaches 67.81%, 74.09%, and 72.45%, respectively. CONCLUSIONS: The proposed segmentation model can accurately segment HRF in SD-OCT images with DR and outperforms traditional methods. Our model may provide reliable segmentations for small lesions in SD-OCT images and may be helpful in the clinical diagnosis of diseases.


Assuntos
Aprendizado Profundo , Retinopatia Diabética/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Tomografia de Coerência Óptica , Olho/diagnóstico por imagem , Humanos
8.
Comput Methods Programs Biomed ; 176: 69-80, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31200913

RESUMO

BACKGROUND AND OBJECTIVE: Quantitative assessment of subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images is crucial for the diagnosis of central serous chorioretinopathy. For the subretinal fluid segmentation, the traditional methods need to segment retinal layers and then segment subretinal fluid. The layer segmentation has a high influence on subretinal fluid segmentation, so we aim to develop a deep learning model to segment subretinal fluid automatically without layer segmentation. METHODS: In this paper, we propose a novel image-to-image double-branched and area-constraint fully convolutional networks (DA-FCN) for segmenting subretinal fluid in SD-OCT images. Firstly, the dataset is extended by mirroring image, which helps to overcome the over-fitting problem in the training stage. Then, double-branched structures are designed to learn the shallow coarse and deep representations from the SD-OCT images. DA-FCN model is directly trained using the image and corresponding pixel-based ground truth. Finally, we introduce a novel supervision mechanism by jointing the area loss LA with the softmax loss LS to learn more representative features. RESULTS: The testing dataset with 52 SD-OCT volumes from 35 eyes of 35 patients is used for the evaluation of the proposed algorithm based on the cross-validation method. For the three criterions, including the true positive volume fraction, dice similarity coefficient, and positive predicative value, our method can obtain the results of (1) 94.3, 95.3, and 96.4 for dataset 1; (2) 97.3, 95.3, and 93.4 for dataset 2; (3) 93.0, 92.8, and 92.8 for dataset 3; (4) 89.7, 90.1, and 92.6 for dataset 4. CONCLUSION: In this work, we propose a novel fully convolutional network for the automatic segmentation of the subretinal fluid. By constructing the double branched structures and area constraint term, our method shows higher segmentation accuracy without layer segmentation compared with other methods.


Assuntos
Coriorretinopatia Serosa Central/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Imageamento Tridimensional , Retina/diagnóstico por imagem , Descolamento Retiniano/diagnóstico por imagem , Tomografia de Coerência Óptica , Algoritmos , Humanos , Modelos Lineares , Probabilidade , Reprodutibilidade dos Testes
9.
Comput Biol Med ; 105: 102-111, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30605812

RESUMO

Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encoder is proposed. Firstly, the axial data of cross-section images were used as samples instead of the projection images of SD-OCT images. Next, a two-stage learning model that includes offline-learning and self-learning was designed based on a stacked sparse auto-encoder to obtain deep discriminative representations. Finally, a fusion strategy was used to refine the segmentation results based on the two-stage learning results. The proposed method was evaluated on two datasets consisting of 55 and 56 cubes, respectively. For the first dataset, our method obtained a mean overlap ratio (OR) of 89.85 ±â€¯6.35% and an absolute area difference (AAD) of 4.79 ±â€¯7.16%. For the second dataset, the mean OR and AAD were 84.48 ±â€¯11.98%, 11.09 ±â€¯13.61%, respectively. Compared with the state-of-the-art algorithms, experiments indicate that the proposed algorithm can provide more accurate segmentation results on these two datasets without using retinal layer segmentation.


Assuntos
Atrofia Geográfica/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador , Aprendizado de Máquina , Retina/diagnóstico por imagem , Tomografia de Coerência Óptica , Humanos
10.
Comput Methods Programs Biomed ; 158: 161-171, 2018 May.
Artigo em Inglês | MEDLINE | ID: mdl-29544782

RESUMO

BACKGROUND AND OBJECTIVE: The measurement of choroidal volume is more related with eye diseases than choroidal thickness, because the choroidal volume can reflect the diseases comprehensively. The purpose is to automatically segment choroid for three-dimensional (3D) spectral domain optical coherence tomography (SD-OCT) images. METHODS: We present a novel choroid segmentation strategy for SD-OCT images by incorporating the enhanced depth imaging OCT (EDI-OCT) images. The down boundary of the choroid, namely choroid-sclera junction (CSJ), is almost invisible in SD-OCT images, while visible in EDI-OCT images. During the SD-OCT imaging, the EDI-OCT images can be generated for the same eye. Thus, we present an EDI-OCT-driven choroid segmentation method for SD-OCT images, where the choroid segmentation results of the EDI-OCT images are used to estimate the average choroidal thickness and to improve the construction of the CSJ feature space of the SD-OCT images. We also present a whole registration method between EDI-OCT and SD-OCT images based on retinal thickness and Bruch's Membrane (BM) position. The CSJ surface is obtained with a 3D graph search in the CSJ feature space. RESULTS: Experimental results with 768 images (6 cubes, 128 B-scan images for each cube) from 2 healthy persons, 2 age-related macular degeneration (AMD) and 2 diabetic retinopathy (DR) patients, and 210 B-scan images from other 8 healthy persons and 21 patients demonstrate that our method can achieve high segmentation accuracy. The mean choroid volume difference and overlap ratio for 6 cubes between our proposed method and outlines drawn by experts were -1.96µm3 and 88.56%, respectively. CONCLUSIONS: Our method is effective for the 3D choroid segmentation of SD-OCT images because the segmentation accuracy and stability are compared with the manual segmentation.


Assuntos
Corioide/diagnóstico por imagem , Retinopatia Diabética/diagnóstico por imagem , Tomografia de Coerência Óptica/métodos , Automação , Estudos de Casos e Controles , Humanos , Aumento da Imagem , Imageamento Tridimensional/métodos , Degeneração Macular/diagnóstico por imagem
11.
Transl Vis Sci Technol ; 7(1): 1, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29302382

RESUMO

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.

12.
Med Phys ; 44(12): 6390-6403, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-28976639

RESUMO

PURPOSE: To develop an automated method based on saliency map of the retinal thickness map to determine foveal center in spectral-domain optical coherence tomography (SD-OCT) images. METHODS: This paper proposes an automatic method for the detection of the foveal center in SD-OCT images. Initially, a retinal thickness map is generated by considering the axial distance between the internal limiting membrane (ILM) and the Bruch's membrane (BM). Both the ILM and BM boundaries are automatically segmented by a known retinal segmentation technique. The macular foveal region is identified as a salient feature in the retinal thickness map, and segmented by the saliency detection method based on a human vision attention model. Finally, the foveal center is identified by searching for the lowest point from the determined macular fovea region. RESULTS: Experimental results in 39 scans from 35 healthy eyes and 58 scans from 29 eyes diagnosed with several stages of age-related macular degeneration (AMD), from mild or intermediate stages to severe dry or wet stages, demonstrated that the proposed method achieves good performance. The mean radial distance error of the automatically detected foveal center locations when compared to consensus manual determination established by repeated sessions from two expert readers was 52 ± 56 µm for the normal eyes and 73 ± 63 µm for AMD eyes. CONCLUSIONS: The proposed algorithm was more effective for detecting the foveal center automatically in SD-OCT images than the state-of-art methods.


Assuntos
Fóvea Central/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Tomografia de Coerência Óptica , Algoritmos , Automação , Fóvea Central/patologia , Humanos , Degeneração Macular/diagnóstico por imagem , Degeneração Macular/patologia
13.
Sci Rep ; 7(1): 1568, 2017 05 08.
Artigo em Inglês | MEDLINE | ID: mdl-28484225

RESUMO

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.


Assuntos
Retinopatia Diabética/patologia , Exsudatos e Transudatos/metabolismo , Imagem Multimodal , Fundo de Olho , Humanos , Processamento de Imagem Assistida por Computador , Índice de Gravidade de Doença , Tomografia de Coerência Óptica
14.
Med Phys ; 43(10): 5464, 2016 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-27782707

RESUMO

PURPOSE: The retinal vessel visualization from spectral-domain optical coherence tomography (SD-OCT) images is important for ocular disease diagnosis and multimodal retinal image processing. The purpose is to display the retinal vessel in a single projection image from 3D SD-OCT images by using the light absorption and shadow characteristics of the retinal vessel. METHODS: The authors present a novel retinal vessel projection method for SD-OCT images, which utilizes the light absorption and shadow characteristics of the retinal vessel, called high-low reflectivity enhancement (HLRE) method. The reflectivity of the retinal vessel increases between the internal limiting membrane and inner nuclear layer-outer plexiform layer (INL-OPL) layers because of the light absorption, and the reflectivity below the retinal vessel decreases because of the influence of the retinal vessel shadow. A retinal vessel mask image generated based on the reflectivity characteristics of the retinal vessel is used to enhance the subvolume projection image restricted between the INL-OPL and Bruch's membrane layers. RESULTS: Experimental results with 22 SD-OCT cubes from 12 patients and 10 normal persons demonstrate that the authors' method is more effective in displaying the retinal vessel than the summed-voxel projection and other five region restriction based projection methods. The average of the mean difference between the retinal vessel and background regions based on their HLRE method is 0.1921. CONCLUSIONS: The proposed HLRE method was more effective for the visualization of the retinal vessels than the state-of-art methods because it provides higher contrast and distinction.


Assuntos
Vasos Retinianos/diagnóstico por imagem , Tomografia de Coerência Óptica/métodos , Humanos , Imageamento Tridimensional , Fenômenos Ópticos
15.
Ophthalmology ; 123(8): 1737-1750, 2016 08.
Artigo em Inglês | MEDLINE | ID: mdl-27262765

RESUMO

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.


Assuntos
Atrofia Geográfica/diagnóstico , Células Fotorreceptoras de Vertebrados/patologia , Drusas Retinianas/diagnóstico por imagem , Epitélio Pigmentado da Retina/patologia , Tomografia de Coerência Óptica , Idoso , Progressão da Doença , Reações Falso-Positivas , Feminino , Seguimentos , Humanos , Masculino , Modelos Biológicos , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Fatores de Risco , Sensibilidade e Especificidade , Tomografia de Coerência Óptica/métodos
16.
Med Phys ; 43(4): 1649, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-27036564

RESUMO

PURPOSE: In 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. METHODS: The 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 Haller's 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. RESULTS: Experimental 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. CONCLUSIONS: Good 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.


Assuntos
Corioide/irrigação sanguínea , Corioide/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Neovascularização Fisiológica , Tomografia de Coerência Óptica , Algoritmos , Humanos , Razão Sinal-Ruído
17.
Biomed Opt Express ; 7(2): 581-600, 2016 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-26977364

RESUMO

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.

18.
Transl Vis Sci Technol ; 4(5): 2, 2015 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-26347016

RESUMO

PURPOSE: To enhance the rapid assessment of geographic atrophy (GA) across the macula in a single projection image generated from three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) scans by introducing a novel restricted summed-area projection (RSAP) technique. METHODS: We describe a novel en face GA visualization technique, the RSAP, by restricting the axial projection of SD-OCT images to the regions beneath the Bruch's membrane (BM) boundary and also considering the choroidal vasculature's influence on GA visualization. The technique analyzes the intensity distribution beneath the retinal pigment epithelium (RPE) layer to fit a cross-sectional surface in the sub-RPE region. The area is taken as the primary GA projection. A median filter is then adopted to smooth the generated GA projection image. The RSAP technique was evaluated in 99 3D SD-OCT data sets from 27 eyes of 21 patients presenting with advanced nonexudative age-related macular degeneration and GA. We used the mean difference between GA and background regions and GA separability metric to measure GA contrast and distinction in the generated images, respectively. We compared our results with two existing GA projection techniques, the summed-voxel projection (SVP) and Sub-RPE Slab techniques. RESULTS: Comparative results demonstrate that the RSAP technique is more effective in displaying GA than the SVP and Sub-RPE Slab. The average of the mean difference between GA and background regions and the GA separability based on SVP, Sub-RPE Slab, and RSAP were 0.129/0.880, 0.238/0.919, and 0.276/0.938, respectively. CONCLUSIONS: The RSAP technique was more effective for GA visualization than the conventional SVP and Sub-RPE Slab techniques. Our technique decreases choroidal vasculature influence on GA projection images by analyzing the intensity distribution characteristics in sub-RPE regions. The generated GA projection image with the RSAP technique has improved contrast and distinction. TRANSLATIONAL RELEVANCE: Our method for automated generation of GA projection images from SD-OCT images may improve the visualization of the macular abnormalities and the management of GA.

19.
Opt Express ; 23(7): 8974-94, 2015 Apr 06.
Artigo em Inglês | MEDLINE | ID: mdl-25968734

RESUMO

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 Bruch's 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.

20.
Comput Biol Med ; 54: 116-28, 2014 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-25240102

RESUMO

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.


Assuntos
Algoritmos , Inteligência Artificial , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Retinoscopia/métodos , Tomografia de Coerência Óptica/métodos , Humanos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Estatística como Assunto
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