Positive Volume Fraction Research Articles

Study on the effects of electrode volume fraction and electrode particle radius on dynamic electrochemical-thermal-aging coupling characteristics of lithium cell based on cylindrical cell design method

Electrode volume fraction and electrode particle radius are important electrode parameters for the lithium cell design and deeply influence the electrochemical-physical processes inside lithium cell, which is still insufficiently clear. In order to obtain the effects of electrode parameters on lithium cell, a combine method of the dynamic electrochemical-thermal-aging coupling model and cylindrical cell design was proposed. Simulation was conducted at charge rate 1C. Cell temperature, current density and Solid-Electrolyte-Interface at ambient temperature 25 °C, and total strain energy density and lithium plating at ambient temperature −5 °C were analyzed. As electrode volume fraction or electrode particle radius increases, maximum temperature and maximum temperature difference of lithium cell increase. When electrode volume fraction increases from 0.3 to 0.7, maximum temperature of cell increases by 2.6 °C at least. Current density peak increases with the increase of the positive electrode volume fraction or electrode particle radius. When electrode volume fraction increases from 0.3 to 0.7, current density peak increases by 44 %∼74 %. As electrode volume fraction increases, total strain energy density Ws decreases in the early stage of charge, while increases in the late stage of charge. As the negative (or positive) electrode particle radius increases, Ws of the negative (or positive) electrode increase, while Ws of the positive (or negative) electrode decreases. Solid-Electrolyte-Interface thickness increases with the increase of the electrode volume fraction or electrode particle radius. The effect range of negative electrode volume on lithium plating is 0.5 ∼ 0.7. The lithium metal thickness increase with the increase of the positive electrode volume fraction. As the negative or positive electrode particle radius increases, the lithium metal thickness increase or decrease, respectively.

Clinical target volume segmentation for stomach cancer by stochastic width deep neural network

Precise segmentation of clinical target volume (CTV) is the key to stomach cancer radiotherapy. We proposed a novel stochastic width-deep neural network (SW-DNN) for better automatically contouring stomach CTV. Stochastic width-deep neural network was an end-to-end approach, of which the core component was a novel SW mechanism that employed shortcut connections between the encoder and decoder in a random manner, and thus the width of the SW-DNN was stochastically adjustable to obtain improved segmentation results. In total, 150 stomach cancer patient computed tomography (CT) cases with the corresponding CTV labels were collected and used to train and evaluate the SW-DNN. Three common quantitative measures: true positive volume fraction (TPVF), positive predictive value (PPV), and Dice similarity coefficient (DSC) were used to evaluate the segmentation accuracy. Clinical target volumes calculated by SW-DNN had significant quantitative advantages over three state-of-the-art methods. The average DSC value of SW-DNN was 2.1%, 2.8%, and 3.6% higher than that of three state-of-the-art methods. The average DSC, TPVF, and PPV values of SW-DNN were 2.1%, 4.0%, and 0.3% higher than that of the corresponding constant width DNN. Stochastic width-deep neural network provided better performance for contouring stomach cancer CTV accurately and efficiently. It is a promising solution in clinical radiotherapy planning for stomach cancer.

RMPPNet: residual multiple pyramid pooling network for subretinal fluid segmentation in SD-OCT images

Automatic assessment of neurosensory retinal detachment (NRD) plays an important role in the diagnosis and treatment for central serous chorioretinopathy (CSC). In this paper, we propose a novel residual multiple pyramid pooling network (RMPPNet) to segment NRD in the spectral-domain optical coherence tomography (SD-OCT) images. Based on the encoder-decoder architecture, RMPPNet can better deal with receptive field and multi-scale features. In the encoder stage, based on the residual architectures, six striding convolutions are utilized to replace the conventional pooling layers to obtain wider receptive fields. To further explore the multi-scale features, three pyramid pooling modules (PPM) are supplemented in the encoder stage. In the decoder stage, we use multiple transpose convolutions to recover the resolution of feature maps and concatenate the feature maps from the encoder for each transpose convolution layer. Finally, for better and faster training, we propose a novel loss function to constrain the different sets between the true label and the prediction label. Three different datasets are utilized to evaluate the proposed model. The first dataset contains 35 cubes from 23 patients, and all the cubes are diagnosed as CSC with only NRD lesions. Based on the first dataset, the second dataset supplements ten normal cubes without NRD lesions. The proposed model obtains a mean dice similarity coefficient 92.6 ± 5.6 and 90.2 ± 20.5, respectively. The last dataset includes 23 cubes from 12 eyes of 12 patients with NRD lesions. The average quantitative results, i.e., mean true positive volume fraction, positive predictive value and dice similarity coefficient, obtained by the proposed model are 96%, 96.45% and 96.2%, respectively. The proposed model can provide a wider receptive field and more abundant multi-scale features to overcome the defects involved in NRD segmentations, such as various size, low contrast, and weak boundaries. Comparing with state-of-the-art methods, the proposed RMPPNet can produce more reliable results for NRD segmentation with higher mean values and lower standard deviations of quantitative criterion, which indicates the practical application for the clinical diagnosis of CSC.

A Unified Homogenization Approach for the Dirichlet Problem in Perforated Domains

We revisit the periodic homogenization of Dirichlet problems for the Laplace operator in perforated domains and establish a unified proof that works for different regimes of hole-cell ratios, which is the ratio between the scaling factor of the holes and that of the periodic cells. The approach is then made quantitative and yields correctors and error estimates for vanishing hole-cell ratios. For a positive volume fraction of holes, the approach is just the standard oscillating test function method; for a vanishing volume fraction of holes, we study asymptotic behaviors of properly rescaled cell problems and use them to build oscillating test functions. Our method reveals how the different regimes are intrinsically connected through the cell problems and the connection with periodic layer potentials.

Coupling multi-physics simulation and response surface methodology for the thermal optimization of ternary prismatic lithium-ion battery

A 3D electrochemical-thermal coupled finite element analysis (FEA) model is established on COMSOL Multiphysics® software for 40 A h ternary prismatic lithium-ion batteries. The input parameters in this model are current C-rate C, battery thickness Thbat, initial battery ambient temperature Tint, positive porous electrode thickness Thpos, positive particle diameter Dpos, positive electrode solid phase volume fraction ε1,pos, respectively. Response surface methodology (RSM) with variance analysis (ANOVA) and parametric scanning is employed for simulating and optimizing the data obtained from the Box-Behnken experimental design based on this model. After that, the optimized parameters are verified to evaluate the reliability of the model on heat generation. The results of response surface optimization show that the optimized model error is less than 0.76% and the final temperature drops is 0.55 K. This novel model is reliable and accurate, which is helpful to improving the electrochemical and thermal performance of the battery. Also, it can provide optimized and feasible solutions for other similar kind of batteries, which offer the reference for design and optimization of the battery management system.

Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images

Background and objectiveQuantitative 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. MethodsIn 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. ResultsThe 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. ConclusionIn 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.

253. An accurate and operator independent method for biological tumour volume segmentation

Purpose The aim of this paper is to develop an operator independent method for biological tumour volume (BTV) delineation from Positron Emission Tomography (PET) images. BTV delineation is challenging because of the low spatial resolution and high noise level in PET images. In addition, BTV varies substantially depending on the method used to segment. Manual delineation is widely-used, but it is strongly user dependent. Methods The proposed method starts with the automatic identification of the PET slice with maximum Standardized Uptake Value (SUV). Then, a user- independent mask is obtained by a rough pre-segmentation step and it is used to perform the local active contour segmentation on the next slices. When the proposed method finds a slice where the mean SUV on the interior of the delineation contour is greater or equal than the mean SUV on the exterior of the delineation contour, the segmentation is automatically stopped. The algorithm is evaluated on four datasets of synthetic lesions considering different ratios between lesion and background radioactivity concentrations. In this way, the actual lesion volumes are known and the segmentation algorithm is evaluated under different contrast ratio scenarios. In addition, BTVs of 25 patient studies have been manually delineated and compared to the proposed method in order to assess its applicability in a clinical environment. Results In phantom experiments, dice similarity coefficient (DSC) rate and true positive volume fraction (TPVF) rate greater than 90% were observed in synthetic lesions with a diameter greater than 17 mm. In clinical cases, TPVF and DSC were 91.00 ± 7.33% and 85.98 ± 3.40%, respectively. Conclusion Our method produces accurate segmentation results in phantom studies. In addition, it is feasible in clinical context and shows good accuracy in realistic conditions, reducing any user interaction.

A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images

BackgroundThere is a growing interest in the use of 18F-FDG PET-CT to monitor tuberculosis (TB) treatment response. However, TB causes complex and widespread pathology, which is challenging to segment and quantify in a reproducible manner.To address this, we developed a technique to standardise uptake (Z-score), segment and quantify tuberculous lung lesions on PET and CT concurrently, in order to track changes over time. We used open source tools and created a MATLAB script. The technique was optimised on a training set of five pulmonary tuberculosis (PTB) cases after standard TB therapy and 15 control patients with lesion-free lungs.ResultsWe compared the proposed method to a fixed threshold (SUV > 1) and manual segmentation by two readers and piloted the technique successfully on scans of five control patients and five PTB cases (four cured and one failed treatment case), at diagnosis and after 1 and 6 months of treatment. There was a better correlation between the Z-score-based segmentation and manual segmentation than SUV > 1 and manual segmentation in terms of overall spatial overlap (measured in Dice similarity coefficient) and specificity (1 minus false positive volume fraction). However, SUV > 1 segmentation appeared more sensitive. Both the Z-score and SUV > 1 showed very low variability when measuring change over time. In addition, total glycolytic activity, calculated using segmentation by Z-score and lesion-to-background ratio, correlated well with traditional total glycolytic activity calculations. The technique quantified various PET and CT parameters, including the total glycolytic activity index, metabolic lesion volume, lesion volumes at different CT densities and combined PET and CT parameters. The quantified metrics showed a marked decrease in the cured cases, with changes already apparent at month one, but remained largely unchanged in the failed treatment case.ConclusionsOur technique is promising to segment and quantify the lung scans of pulmonary tuberculosis patients in a semi-automatic manner, appropriate for measuring treatment response. Further validation is required in larger cohorts.

Improved graph-cut segmentation for ultrasound liver cyst image

An optimal contour segmentation for ultrasonic liver cyst image is presented through combining graph-based method with particle swarm optimization (PSO) in this paper. After automatic selecting the region of interest (ROI) for ultrasonic liver cyst image, our method developed firstly a kind of multiple classes merging scheme by jointing the graph-based segmented result with the intensity of original ultrasound image. Then the evaluation function in the PSO was modified to optimize the parameter. Finally, the liver cysts were segmented according to the optimized parameter. In the experiment, we tested the influence of weight value on the improved method. And five indicators, which included Hausdorff distance (HD), mean absolute distance (MD), true positive volume fraction (TPVF), false-negative volume fraction (FNVF) and false-positive volume fraction (FPVF), were estimated to verify the improved method. Experimental results have validated that the improved method may extract successfully and accurately the contour of liver cyst.

Dental X-ray Image Segmentation using Gaussian Kernel-Based in Conditional Spatial Fuzzy C-means

Dental X-ray image segmentation is a difficult task because of intensity inhomogeneities among various regions, low image quality due to noise and low contrast errors of data scanning. In this paper, we proposed a new conditional spatial fuzzy C-means algorithm with Gaussian kernel function to facilitate dental X-ray image segmentation. The Gaussian kernel function is used as an objective function of conditional spatial fuzzy C-means algorithm to substitute the Euclidian distance. Performance evaluation of the proposed algorithm was carried on dental X-ray from different teeth of some panoramic radiographs. The average of false negative fraction (FNF) and false positive fraction (TPF) values using proposed algorithm better than conditional spatial fuzzy C-means algorithm but vise versa for true positive volume fraction (FPF) value. The segmentation result of the proposed algorithm effectively recognizes tooth region as main part of the dental X-ray image.

Fast segmentation of kidney components using random forests and ferns.

This paper studies the feasibility of developing a fast and accurate automatic kidney component segmentation method. The proposed method segments the kidney into four components: renal cortex, renal column, renal medulla, and renal pelvis. In this article, we have proposed a highly efficient approach which strategically combines random forests and random ferns methods to segment the kidney into four components: renal cortex, renal column, renal medulla, and renal pelvis. The proposed method is designed following a coarse-to-fine strategy. The initial segmentation applies random forests and random ferns with a variety of features, and combines their results to obtain a coarse renal cortex region. Then the fine segmentation of four kidney components is achieved using the weighted forests-ferns approach with the well-designed potential energy features which are calculated based on the initial segmentation result. The proposed method was validated on a dataset with 37 contrast-enhanced CT images. Evaluation indices including Dice similarity coefficient (DSC), true positive volume fraction (TPVF), and false positive volume fraction (FPVF) are used to assess the segmentation accuracy. The proposed method was implemented and tested on a 64-bit system computer (Intel Core i7-3770 CPU, 3.4 GHz and 8 GB RAM). The experimental results demonstrated the high accuracy and efficiency for segmenting the kidney components: the mean Dice similarity coefficients were 89.85%, 80.60%, 86.63%, and 77.75% for renal cortex, column, medulla, and pelvis, respectively, for right and left kidneys. The computational time of segmenting the whole kidney into four components was about 3 s. The experimental results showed the feasibility and efficacy of the proposed automatic kidney component segmentation method. The proposed method applied an efficient weighted strategy to combine random forests and ferns, making full use of the advantages of both methods. The novel potential energy features help random forests effectively segment the kidney components and the background. The high accuracy and efficiency of our method make it practicable in clinical applications.

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.

Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.

Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management.

Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole.

Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate the initialization results. Second, an automated shape-constrained graph cut algorithm is applied to obtain the refined results. Finally, cyst area information is used to remove false positives (FPs). The method was evaluated on 19 eyes with coexistence of CMEs and MH from 18 subjects. The true positive volume fraction, FP volume fraction, dice similarity coefficient, and accuracy rate for CMEs segmentation were 81.0%±7.8%, 0.80%±0.63%, 80.9%±5.7%, and 99.7%±0.1%, respectively.

On the Growth of a Particle Coalescing in a Poisson Distribution of Obstacles

In this paper we consider the coalescence dynamics of a tagged particle moving in a random distribution of particles with volumes independently distributed according to a probability distribution (CTP model). We provide a rigorous derivation of a kinetic equation for the probability density for the size and position of the tagged particle in the kinetic limit where the volume fraction $\phi$ filled by the background of particles tends to zero. Moreover, we prove that the particle system, i.e. CTP model, is well posed for a small but positive volume fraction with probability one as long as the the distribution of the particle sizes is compactly supported.

A $$\varvec{2\times 2}$$ hyperbolic system modelling incompressible two phase flows: theory and numerics

We propose a \(2\times 2\) hyperbolic system of conservation laws to model the dynamics of two incompressible fluids in mechanical disequilibrium. In the theoretical part of the paper we show that this 1D system is not strictly hyperbolic, that the characteristic speed can not a priori be ordered and that the characteristic fields are neither genuinely nonlinear, nor linearly degenerate. We nevertheless prove the existence and uniqueness of an admissible solution to the Riemann problem. This solution remains bounded with positive volume fractions even when one the phases vanishes. We conclude that the multiphase/single phase transition does not imply mechanical equilibrium but displays a non classical wave structure. In the numerical part of the paper we propose some approximate Riemann solvers to simulate the model, especially the multiphase/single phase transition. The classical Riemann solvers have been considered as Godunov scheme, Roe scheme with or without entropy fix. We also propose an in-cell discontinuous reconstruction method which proves to be successful, whereas the other schemes may show some spurious oscillations in some Riemann problem. Finally, as an application we study and simulate the problem of phase separation by gravity.

Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images With Neurosensory Retinal Detachment Guided by Enface Fundus Imaging.

Accurate segmentation of neurosensory retinal detachment (NRD) associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) is vital for the assessment of central serous chorioretinopathy (CSC). A novel two-stage segmentation algorithm was proposed, guided by Enface fundus imaging. In the first stage, Enface fundus image was segmented using thickness map prior to detecting the fluid-associated abnormalities with diffuse boundaries. In the second stage, the locations of the abnormalities were used to restrict the spatial extent of the fluid region, and a fuzzy level set method with a spatial smoothness constraint was applied to subretinal fluid segmentation in the SD-OCT scans. Experimental results from 31 retinal SD-OCT volumes with CSC demonstrate that our method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), and positive predicative value (PPV) of 94.3%, 0.97%, and 93.6%, respectively, for NRD regions. Our approach can also discriminate NRD-associated subretinal fluid from subretinal pigment epithelium fluid associated with pigment epithelial detachment with a TPVF, FPVF, and PPV of 93.8%, 0.40%, and 90.5%, respectively. We report a fully automatic method for the segmentation of subretinal fluid. Our method shows the potential to improve clinical therapy for CSC.

Interactive iterative relative fuzzy connectedness lung segmentation on thoracic 4D dynamic MR images.

Lung delineation via dynamic 4D thoracic magnetic resonance imaging (MRI) is necessary for quantitative image analysis for studying pediatric respiratory diseases such as thoracic insufficiency syndrome (TIS). This task is very challenging because of the often-extreme malformations of the thorax in TIS, lack of signal from bone and connective tissues resulting in inadequate image quality, abnormal thoracic dynamics, and the inability of the patients to cooperate with the protocol needed to get good quality images. We propose an interactive fuzzy connectedness approach as a potential practical solution to this difficult problem. Manual segmentation is too labor intensive especially due to the 4D nature of the data and can lead to low repeatability of the segmentation results. Registration-based approaches are somewhat inefficient and may produce inaccurate results due to accumulated registration errors and inadequate boundary information. The proposed approach works in a manner resembling the Iterative Livewire tool but uses iterative relative fuzzy connectedness (IRFC) as the delineation engine. Seeds needed by IRFC are set manually and are propagated from slice-to-slice, decreasing the needed human labor, and then a fuzzy connectedness map is automatically calculated almost instantaneously. If the segmentation is acceptable, the user selects "next" slice. Otherwise, the seeds are refined and the process continues. Although human interaction is needed, an advantage of the method is the high level of efficient user-control on the process and non-necessity to refine the results. Dynamic MRI sequences from 5 pediatric TIS patients involving 39 3D spatial volumes are used to evaluate the proposed approach. The method is compared to two other IRFC strategies with a higher level of automation. The proposed method yields an overall true positive and false positive volume fraction of 0.91 and 0.03, respectively, and Hausdorff boundary distance of 2 mm.

A Framework for Classification and Segmentation of Branch Retinal Artery Occlusion in SD-OCT.

Branch retinal artery occlusion (BRAO) is an ocular emergency, which could lead to blindness. Quantitative analysis of the BRAO region in the retina is necessary for the assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to segment BRAO regions based on 3D spectral-domain optical coherence tomography (SD-OCT) images. To the best of our knowledge, this is the first automatic 3D BRAO segmentation framework. First, the input 3D image is automatically classified into BRAO of acute phase and BRAO of chronic phase or normal retina using an AdaBoost classifier based on combining local structural, intensity, textural features with our new feature distribution analyzing strategy. Then, BRAO regions of acute phase and chronic phase are segmented separately. A thickness model is built to segment BRAO in the chronic phase. While for segmenting BRAO in the acute phase, a two-step segmentation strategy is performed: rough initialization and refine segmentation. The proposed method was tested on SD-OCT images of 35 patients (12 BRAO acute phase, 11 BRAO chronic phase, and 12 normal eyes) using the leave-one-out strategy. The classification accuracy for BRAO acute phase, BRAO chronic phase, and normal retina were 100%, 90.9%, and 91.7%, respectively. The overall true positive volume fraction (TPVF) and false positive volume fraction (FPVF) for the acute phase were 91.1% and 5.5% and for the chronic phase were 92.7% and 8.4%, respectively.

3D Curvelet-Based Segmentation and Quantification of Drusen in Optical Coherence Tomography Images

Spectral-Domain Optical Coherence Tomography (SD-OCT) is a widely used interferometric diagnostic technique in ophthalmology that provides novel in vivo information of depth-resolved inner and outer retinal structures. This imaging modality can assist clinicians in monitoring the progression of Age-related Macular Degeneration (AMD) by providing high-resolution visualization of drusen. Quantitative tools for assessing drusen volume that are indicative of AMD progression may lead to appropriate metrics for selecting treatment protocols. To address this need, a fully automated algorithm was developed to segment drusen area and volume from SD-OCT images. The proposed algorithm consists of three parts: (1) preprocessing, which includes creating binary mask and removing possible highly reflective posterior hyaloid that is used in accurate detection of inner segment/outer segment (IS/OS) junction layer and Bruch’s membrane (BM) retinal layers; (2) coarse segmentation, in which 3D curvelet transform and graph theory are employed to get the possible candidate drusenoid regions; (3) fine segmentation, in which morphological operators are used to remove falsely extracted elongated structures and get the refined segmentation results. The proposed method was evaluated in 20 publically available volumetric scans acquired by using Bioptigen spectral-domain ophthalmic imaging system. The average true positive and false positive volume fractions (TPVF and FPVF) for the segmentation of drusenoid regions were found to be 89.15% ± 3.76 and 0.17% ± .18%, respectively.