Determination of foveal avascular zone parameters using a new location-aware deep-learning method

Sobhaninia, Zahra; Danesh, Hajar; Kafieh, Rahele; Balaji, J. Jothi; Lakshminarayanan, Vasudevan

doi:10.1117/12.2594152

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…From this algorithm, 15 parametric dimensions of the FAZ can be measured. 22 These are: Area (mm 2 ), Diameter diameter (mm), Major axis length (mm), Minor axis length (mm), perimeter (mm), Eccentricity (mm), F min (mm), F max (mm), Inner circle radius (mm), Circumcircle radius (mm), Orientation ( 0 ) Tortuosity†, VAD†, VDI (mm), and Circularity index†. These parametric dimensions are compared between the Inbuilt Algorithm Method (IAM), Clinical expert method (CEM), and New automated method (NAM) images to analyze the algorithm’s robustness.…”

Section: Methodsmentioning

confidence: 99%

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone

Viekash¹,

Balaji²,

Lakshminarayanan³

2021

OPTH

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Introduction Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions. Therefore, it is important to segment and quantify the FAZs dimensions accurately. To the best of our knowledge, there is no automated tool or algorithms available to segment the FAZ’s deep retinal layer. The paper describes a new open-access software with a Graphical User Interface (GUI) and compares the results with the ground truth (manual segmentation). Methods Ninety-three healthy normal subjects included 30 emmetropia and 63 myopic subjects without any sight-threatening retinal conditions, were included in the study. The 6mm x 6mm using the Angioplex protocol (Cirrus 5000 Carl Zeiss Meditec Inc., Dublin, CA) was used, and all the images were aligned with the centre of the fovea. Each FAZ image corresponding to dimensions 420×420 pixels were used in this study. These FAZ image dimensions for the superficial and deep layers were quantified using the New Automated Software Method (NAM). The NAM-based FAZ dimensions were validated with the ground truth. Results The age distribution for all 93 subjects was 28.02 ± 10.79 (range, 10.0–66.0) years. For normal subjects mean ± SD age distribution was 32.13 ± 16.27 years. Similarly, the myopia age distribution was 26.06 ± 6.06 years. The NAM had an accuracy of 91.40%. Moreover, the NAM on superficial layer FAZ gave a Dice Similarity Coefficient (DSC) score of 0.94 and Structural Similarity Index Metric (SSIM) of 0.97, while the NAM on deep layer FAZ gave a DSC score of 0.96 and SSIM of 0.98. Conclusion A clinician-based GUI software was designed and tested on the FAZ images from deep and superficial layers. The NAM outperformed the device’s inbuilt algorithm when measuring the superficial layer. This open-source software package is in the public domain and can be downloaded online.

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Section: Methodsmentioning

confidence: 99%

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone

Viekash¹,

Balaji²,

Lakshminarayanan³

2021

OPTH

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“…Recently, in-depth learning has covered a wide range of applications of NLP [18]. Moreover, recurrent neural network (RNN) and CNN are applied as two strong arms of deep learning in feature extraction [20,21]. e behavior of deep learning methods with question-answer pairs is divided into two categories.…”

Section: Related Workmentioning

confidence: 99%

“…(3) while ≤ MaxItrdo (4) //Employed Bee Phase (5) fori � 1 to BNdo (6) Produce new solution x new using (6) (7) Calculate the fitness f new for x new (8) Replace x new with x i if better (9) end for (10) Calculate the probability p for every solution in X using (7) (11) //Onlooker Bee Phase (12) fori � 1 to BNdo (13) if rand (0, 1) < p i then (14) Produce new solution x new using (6) (15) Calculate the fitness f new for x new (16) Replace x new with x i if better (17) end if (18) end for (19) //Scout Bee Phase (20) If an abandoned solution is found, replace it with the solution produced by (6) (21) Put the best solution ever in x best (22) Itr � Itr + 1. ( 23) end while (24) returnx best ALGORITHM 1: Pseudocode of the ABC algorithm.…”

Section: The Framework Of Rlas-biabcmentioning

confidence: 99%

“…Second, feature extraction and handmade rules are not flexible, leading to inferior generalization capability. After the appearance of deep learning, many problems in many domains [17][18][19][20][21][22][23], including AS, have been overshadowed by it. Deep learning-based methods for AS usually employ a convolutional neural network (CNN) [24] or LSTM to grab semantic features on various levels.…”

Section: Introductionmentioning

confidence: 99%

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RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm

Gharagozlou

Mohammadzadeh

Bastanfard

et al. 2022

Computational Intelligence and Neuroscience

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Answer selection (AS) is a critical subtask of the open-domain question answering (QA) problem. The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers (BERT) word embedding, enriched by an improved artificial bee colony (ABC) algorithm for pretraining and a reinforcement learning-based algorithm for training backpropagation (BP) algorithm. BERT can be comprised in downstream work and fine-tuned as a united task-specific architecture, and the pretrained BERT model can grab different linguistic effects. Existing algorithms typically train the AS model with positive-negative pairs for a two-class classifier. A positive pair contains a question and a genuine answer, while a negative one includes a question and a fake answer. The output should be one for positive and zero for negative pairs. Typically, negative pairs are more than positive, leading to an imbalanced classification that drastically reduces system performance. To deal with it, we define classification as a sequential decision-making process in which the agent takes a sample at each step and classifies it. For each classification operation, the agent receives a reward, in which the prize of the majority class is less than the reward of the minority class. Ultimately, the agent finds the optimal value for the policy weights. We initialize the policy weights with the improved ABC algorithm. The initial value technique can prevent problems such as getting stuck in the local optimum. Although ABC serves well in most tasks, there is still a weakness in the ABC algorithm that disregards the fitness of related pairs of individuals in discovering a neighboring food source position. Therefore, this paper also proposes a mutual learning technique that modifies the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. We tested our model on three datasets, LegalQA, TrecQA, and WikiQA, and the results show that RLAS-BIABC can be recognized as a state-of-the-art method.

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“…Graph-based approaches have exhibited better performance than the other methods, but they suffer from a lack of interaction constraints with high processing time (12,14). Recently, deep learning-based architectures have been successfully applied in the eld of biomedical image segmentation of the retina, liver, brain, pancreas, heart, and other structures (16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images Low Contrast Sclerochoroidal Junction Using Deep Learning

Arian

Mahmoudi

Riazi-Esfahani

et al. 2021

Preprint

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Choroidal vascularity index (CVI) is a new biomarker defined for retinal optical coherence tomography (OCT) images for measuring and evaluating the choroidal vascular structure. CVI is the ratio of the choroidal luminal area (LA) to the total choroidal area (TCA). The automatic calculation of this index is important for ophthalmologists but has not yet been explored. In this study, we proposed a fully automated method based on deep learning for calculating CVI in three main steps: 1- segmentation of the choroidal boundary, 2- detection of the choroidal luminal vessels, and 3- computation of the CVI. The proposed method is evaluated in complex situations like the presence of diabetic retinopathy and pachychoroid spectrum. In pachychoroid spectrum, the choroid is thickened, and the boundary between choroid and sclera (sclerochoroidal junction) is blurred, which makes the segmentation more challenging. The proposed method is designed based on the U-Net model, and a new loss function is proposed to overcome the segmentation problems. The vascular LA is then calculated using Niblack’s local thresholding method, and the CVI value is finally computed. The experimental results for the segmentation stage with the best-performing model and the proposed loss function were used showed dice coefficients of 0.941 and 0.936 in diabetic retinopathy and pachychoroid spectrum patients, respectively. The unsigned boundary localization errors in the presence of diabetic retinopathy were 0.0020 and 0.0138 pixels for the BM boundary and sclerochoroidal junction, respectively. Similarly, the unsigned errors in the presence of pachychoroid spectrum were 0.0072 and 0.0254 pixels for BM and sclerochoroidal junction. The performance of the proposed method for calculating CVI was evaluated; the Bland-Altman plot indicated acceptable agreement between the values allocated by experts and the proposed method in the presence of diabetic retinopathy and pachychoroid spectrum.

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Determination of foveal avascular zone parameters using a new location-aware deep-learning method

Cited by 6 publications

References 25 publications

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone

FAZSeg: A New Software for Quantification of the Foveal Avascular Zone

RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm

Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images Low Contrast Sclerochoroidal Junction Using Deep Learning

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