Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection.
ABSTRACT: This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.
Project description:Disruption of external limiting membrane (ELM) integrity on spectral-domain optical coherence tomography (SD-OCT) is associated with lower visual acuity outcomes in patients suffering from diabetic macular edema (DME). However, no automated methods to detect ELM and/or determine its integrity from SD-OCT exist.Sixteen subjects diagnosed with clinically significant DME (CSME) were included and underwent macula-centered SD-OCT (512 × 19 × 496 voxels). Sixteen subjects without retinal thickening and normal acuity were also scanned (200 × 200 × 1024 voxels). Automated quantification of ELM disruption was achieved as follows. First, 11 surfaces were automatically segmented using our standard 3-D graph-search approach, and the subvolume between surface 6 and 11 containing the ELM region was flattened based on the segmented retinal pigment epithelium (RPE) layer. A second, edge-based graph-search surface-detection method segmented the ELM region in close proximity "above" the RPE, and each ELM A-scan was classified as disrupted or nondisrupted based on six texture features in the vicinity of the ELM surface. The vessel silhouettes were considered in the disruption classification process to avoid false detections of ELM disruption.In subjects with CSME, large areas of disrupted ELM were present. In normal subjects, ELM was largely intact. The mean and 95% confidence interval (CI) of the detected disruption area volume for normal and CSME subjects were mean(normal) = 0.00087 mm(3) and CI(normal) = (0.00074, 0.00100), and mean(CSME) = 0.00461 mm(3) and CI(CSME) = (0.00347, 0.00576) mm(3), respectively.In this preliminary study, we were able to show that automated quantification of ELM disruption is feasible and can differentiate continuous ELM in normal subjects from disrupted ELM in subjects with CSME. We have started determining the relationships of quantitative ELM disruption markers to visual outcome in patients undergoing treatment for CSME.
Project description:Diabetic macular edema (DME) is the abnormal accumulation of fluid in the subretinal or intraretinal spaces in the macula in patients with diabetic retinopathy and leads to severely impaired central vision. Technical developments in retinal imaging systems have led to many advances in the study of DME. In particular, optical coherence tomography (OCT) can provide longitudinal and microstructural analysis of the macula. A comprehensive review was provided regarding the role of inflammation using OCT-based classification of DME and current and ongoing therapeutic approaches. In this review, we first describe the pathogenesis of DME, then discuss the classification of DME based on OCT findings and the association of different types of DME with inflammation, and finally describe current and ongoing therapeutic approaches using OCT-based classification of DME. Inflammation has an important role in the pathogenesis of DME, but its role appears to differ among the DME phenotypes, as determined by OCT. It is important to determine how the different DME subtypes respond to intravitreal injections of steroids, antivascular endothelial growth factor agents, and other drugs to improve prognosis and responsiveness to treatment.
Project description:Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification performance. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification performance of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy participants performed 6 sessions of 15 gait trials for 1 day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRFs). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each preprocessing step were analyzed by comparing their prediction performance in a six-session classification using Support Vector Machines, Random Forest Classifiers, Multi-Layer Perceptrons, and Convolutional Neural Networks. The results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction performance of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.
Project description:Inflammation contributes significantly to the pathogenesis of diabetic macular edema (DME). In particular, retinal microglia demonstrate increased activation and aggregation in areas of DME. Study authors investigated the safety and potential efficacy of oral minocycline, a drug capable of inhibiting microglial activation, in the treatment of DME.A single-center, prospective, open-label phase I/II clinical trial enrolled five participants with fovea-involving DME who received oral minocycline 100 mg twice daily for 6 months. Main outcome measurements included best-corrected visual acuity (BCVA), central retinal subfield thickness (CST), and central macular volume using spectral domain optical coherence tomography (SD-OCT) and late leakage on fluorescein angiography (FA).Findings indicated that the study drug was well tolerated and not associated with significant safety issues. In study eyes, mean BCVA improved continuously from baseline at 1, 2, 4, and 6 months by +1.0, +4.0, +4.0, and +5.8 letters, respectively, while mean retinal thickness (CST) on OCT decreased by -2.9%, -5.7%, -13.9, and -8.1% for the same time points. At month 6, mean area of late leakage on FA decreased by -34.4% in study eyes. Mean changes in contralateral fellow eyes also demonstrated similar trends. Improvements in outcome measures were not correlated with concurrent changes in systemic factors.In this pilot proof-of-concept study of DME, minocycline as primary treatment was associated with improved visual function, central macular edema, and vascular leakage, comparing favorably with historical controls from previous studies. Microglial inhibition with oral minocycline may be a promising therapeutic strategy targeting the inflammatory etiology of DME. (ClinicalTrials.gov number, NCT01120899.).
Project description:BACKGROUND: In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor's nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor's experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. METHODS: A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. RESULTS: A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naïve Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm. CONCLUSIONS: A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.
Project description:Purpose:To report the impact of baseline central retinal thickness (CRT) on outcomes in patients with diabetic macular edema (DME) in VIVID-DME and VISTA-DME. Methods:Post hoc analyses of two randomized controlled trials in which 862 DME patients were randomized 1?:?1?:?1 to treatment with intravitreal aflibercept 2.0?mg every 4 weeks (2q4), intravitreal aflibercept 2.0?mg every 8 weeks after five initial monthly doses (2q8), or macular laser photocoagulation at baseline and as needed. We compared visual and anatomical outcomes in subgroups of patients with baseline CRT?<?400??m and ?400??m. Results:At weeks 52 and 100, outcomes with intravitreal aflibercept 2q4 and 2q8 were superior to those in laser control-treated patients regardless of baseline CRT. When looked at in a binary fashion, the treatment effect of intravitreal aflibercept versus laser was not significantly better in the ?400??m than the <400??m group; when looked at as a continuous variable, baseline CRT seemed to have an impact on the treatment effect of intravitreal aflibercept versus laser. Conclusions:Post hoc analyses of VIVID-DME and VISTA-DME demonstrated the benefits of intravitreal aflibercept treatment in DME patients with baseline CRT?<?400??m and ?400??m. This trial is registered with NCT01331681 and NCT01363440.
Project description:Our purpose was to compare the impact in diabetic macula edema (DME) of two intravitreal drugs (0.5 mg ranibizumab vs. 8 mg triamcinolone) on changes in retinal morphology in spectral-domain optical coherence tomography (SD OCT) images, color fundus photography (CF) and fluorescein angiography (FA) images during a 1-year follow-up.Post hoc analysis was conducted of morphologic characteristics in OCT, FA and CF images of eyes with a center involving DME that were included in a prospective double-masked randomized trial. Eligible patients were divided at random into two groups receiving either pro re nata treatment with 0.5 mg ranibizumab or 8 mg triamcinolone after a fixed loading dose. OCT and CF images were acquired at monthly visits and FA images every three months.Twenty-five eyes of 25 patients (ranibizumab: n = 10; triamcinolone: n = 15) were included in this study. Patients treated with ranibizumab showed better visual acuity results after 12 months than patients receiving triamcinolone (p = 0.015) although edema reduction was similar (p = 0.426) in both groups. The initial effect on macular edema shedding after a single ranibizumab injection could be amplified with the following two injections of the loading dose. After a single injection of triamcinolone the beneficial initial effect on the macula edema faded within 3 months. Subretinal fluid and INL cystoid spaces diminished early in the course of treatment while fluid accumulation in the ONL seemed to be more persistent in both treatment arms. In FA, the area of leakage diminished significantly in both treatment arms. After repeated injections the morphologic OCT and FA characteristics of the treatment arms converged.Despite the higher dosage of triamcinolone, both therapies were safe and effective for treating diabetic macular edema. Fluid accumulation in the INL and subretinal space was more responsive to therapy than fluid accumulation in the ONL. Clinicaltrials.gov : NCT00682539.
Project description:Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results. First, in preprocessing stage, hematoxylin-eosin (H&E) pathological images are enhanced using bilateral filter and each HCC image patch is obtained under the guidance of pathologists. Then, after extracting the complete features of each patch, a new sparse contribution (SC) feature selection model is established to select the beneficial features for each classifier. Finally, a concave-convex variation method is developed to improve the performance of classifiers. Experiments using 1260 HCC image patches demonstrate that our proposed CCV classifiers have improved greatly compared to each original classifier and CCV-random forest (CCV-RF) performs the best for HCC image recognition.
Project description:Background:Optical coherence tomography (OCT) is an innovative imaging technique that generates high-resolution intracoronary images. In the last few years, the need for more precise analysis regarding coronary artery disease to achieve optimal treatment has made intravascular imaging an area of primary importance in interventional cardiology. One of the main challenges in OCT image analysis is the accurate detection of lumen which is significant for the further prognosis. Method:In this research, we present a new approach to the segmentation of lumen in OCT images. The proposed work is focused on designing an efficient automatic algorithm containing the following steps: preprocessing (artifacts removal: speckle noise, circular rings, and guide wire), conversion between polar and Cartesian coordinates, and segmentation algorithm. Results:The implemented method was tasted on 667 OCT frames. The lumen border was extracted with a high correlation compared to the ground truth: 0.97 ICC (0.97-0.98). Conclusions:Proposed algorithm allows for fully automated lumen segmentation on optical coherence tomography images. This tool may be applied to automated quantitative lumen analysis.
Project description:<h4>Aims</h4>To study the association between peripheral blood metabolic and inflammatory factors and presence of diabetic macular edema (DME) and its related anatomic features in type 2 diabetic mellitus (T2DM) patients.<h4>Material and methods</h4>Observational cross-sectional study on a proof of concept basis. Seventy-six T2DM included patients were divided based on the presence (n = 58) or absence of DME (n = 18) according to optical coherence tomography (OCT). Ultra-widefield fluorescein angiography (UWFA) was performed in DME patients. Fasting peripheral blood sample testing included glycemia, glycated hemoglobin, creatinin and lipid levels among others. Serum levels of a broad panel of cytokines and inflammatory mediators were also analysed. OCT findings included central subfoveal thickness, diffuse retinal thickness (DRT), cystoid macular edema (CME), serous retinal detachment and epirretinal membrane. UWFA items included pattern of DME, presence of peripheral retinal ischemia and enlarged foveal avascular zone (FAZ).<h4>Results</h4>Metabolic and inflammatory factors did not statistically differ between groups. However, several inflammatory mediators did associate to certain ocular items of DME cases: IL-6 was significantly higher in patients with DRT (p = 0.044), IL-10 was decreased in patients with CME (p = 0.012), and higher IL-8 (p = 0.031) and VEGF levels (p = 0.031) were observed in patients with enlarged FAZ.<h4>Conclusion</h4>Inflammatory and metabolic peripheral blood factors in T2DM may not be differentially associated to DME when compared to non-DME cases. However, some OCT and UWFA features of DME such as DRT, CME and enlarged FAZ may be associated to certain systemic inflammatory mediators.