scholarly journals Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Yifan Xiang ◽  
Jingjing Chen ◽  
Fabao Xu ◽  
Zhuoling Lin ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Intelligent System ◽  
Ground Truth ◽  
Visual Development ◽  
Machine Learning Algorithms ◽  
Optical Coherence ◽  
Visual Prognosis ◽  
Intervention Plan ◽  
Long Term Prediction ◽  
Mean Square Errors

The results of visual prediction reflect the tendency and speed of visual development during a future period, based on which ophthalmologists and guardians can know the potential visual prognosis in advance, decide on an intervention plan, and contribute to visual development. In our study, we developed an intelligent system based on the features of optical coherence tomography images for long-term prediction of best corrected visual acuity (BCVA) 3 and 5 years in advance. Two hundred eyes of 132 patients were included. Six machine learning algorithms were applied. In the BCVA predictions, small errors within two lines of the visual chart were achieved. The mean absolute errors (MAEs) between the prediction results and ground truth were 0.1482–0.2117 logMAR for 3-year predictions and 0.1198–0.1845 logMAR for 5-year predictions; the root mean square errors (RMSEs) were 0.1916–0.2942 logMAR for 3-year predictions and 0.1692–0.2537 logMAR for 5-year predictions. This is the first study to predict post-therapeutic BCVAs in young children. This work establishes a reliable method to predict prognosis 5 years in advance. The application of our research contributes to the design of visual intervention plans and visual prognosis.

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

Automatic Plaque Segmentation in Coronary Optical Coherence Tomography Images

2019 ◽  
Vol 33 (14) ◽  
pp. 1954035 ◽  
Author(s):  
Huaqi Zhang ◽  
Guanglei Wang ◽  
Yan Li ◽  
Feng Lin ◽  
Yechen Han ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Coronary Artery Stenosis ◽  
Ground Truth ◽  
High Accuracy ◽  
Similarity Coefficient ◽  
Optical Coherence ◽  
Jaccard Similarity ◽  
Plaque Area ◽  
Adaptive Weight ◽  
Different Depths

Coronary optical coherence tomography (OCT) is a new high-resolution intravascular imaging technology that clearly depicts coronary artery stenosis and plaque information. Study of coronary OCT images is of significance in the diagnosis of coronary atherosclerotic heart disease (CAD). We introduce a new method based on the convolutional neural network (CNN) and an improved random walk (RW) algorithm for the recognition and segmentation of calcified, lipid and fibrotic plaque in coronary OCT images. First, we design CNN with three different depths (2, 4 or 6 convolutional layers) to perform the automatic recognition and select the optimal CNN model. Then, we device an improved RW algorithm. According to the gray-level distribution characteristics of coronary OCT images, the weights of intensity and texture term in the weight function of RW algorithm are adjusted by an adaptive weight. Finally, we apply mathematical morphology in combination with two RWs to accurately segment the plaque area. Compared with the ground truth of clinical segmentation results, the Jaccard similarity coefficient (JSC) of calcified and lipid plaque segmentation results is 0.864, the average symmetric contour distance (ASCD) is 0.375[Formula: see text]mm, the JSC and ASCD reliabilities are 88.33% and 92.50% respectively. The JSC of fibrotic plaque is 0.876, the ASCD is 0.349[Formula: see text]mm, the JSC and ASCD reliabilities are 90.83% and 95.83% respectively. In addition, the average segmentation time (AST) does not exceed 5 s. Reliable and significantly improved results have been achieved in this study. Compared with the CNN, traditional RW algorithm and other methods. The proposed method has the advantages of fast segmentation, high accuracy and reliability, and holds promise as an aid to doctors in the diagnosis of CAD.

scholarly journals Expert-level Automated Biomarker Identification in Optical Coherence Tomography Scans

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas Kurmann ◽  
Siqing Yu ◽  
Pablo Márquez-Neila ◽  
Andreas Ebneter ◽  
Martin Zinkernagel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Critical Role ◽  
Cost Effective ◽  
Automated Analysis ◽  
Machine Learning Algorithms ◽  
Three Dimensions ◽  
Optical Coherence ◽  
Clinical Routine ◽  
Wide Range

Abstract In ophthalmology, retinal biological markers, or biomarkers, play a critical role in the management of chronic eye conditions and in the development of new therapeutics. While many imaging technologies used today can visualize these, Optical Coherence Tomography (OCT) is often the tool of choice due to its ability to image retinal structures in three dimensions at micrometer resolution. But with widespread use in clinical routine, and growing prevalence in chronic retinal conditions, the quantity of scans acquired worldwide is surpassing the capacity of retinal specialists to inspect these in meaningful ways. Instead, automated analysis of scans using machine learning algorithms provide a cost effective and reliable alternative to assist ophthalmologists in clinical routine and research. We present a machine learning method capable of consistently identifying a wide range of common retinal biomarkers from OCT scans. Our approach avoids the need for costly segmentation annotations and allows scans to be characterized by biomarker distributions. These can then be used to classify scans based on their underlying pathology in a device-independent way.

scholarly journals Cyst Detection and Motion Artifact Elimination in Enface Optical Coherence Tomography Angiograms

2020 ◽  
Vol 10 (11) ◽  
pp. 3994
Author(s):  
Emanuele Torti ◽  
Caterina Toma ◽  
Stela Vujosevic ◽  
Paolo Nucci ◽  
Stefano De Cillà ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optical Coherence Tomography Angiography ◽  
Ground Truth ◽  
Motion Artifact ◽  
Jaccard Index ◽  
Motion Artifacts ◽  
Optical Coherence ◽  
Medical Doctors ◽  
Crucial Importance ◽  
Novel Algorithm

The correct detection of cysts in Optical Coherence Tomography Angiography images is of crucial importance for allowing reliable quantitative evaluation in patients with macular edema. However, this is a challenging task, since the commercially available software only allows manual cysts delineation. Moreover, even small eye movements can cause motion artifacts that are not always compensated by the commercial software. In this paper, we propose a novel algorithm based on the use of filters and morphological operators, to eliminate the motion artifacts and delineate the cysts contours/borders. The method has been validated on a dataset including 194 images from 30 patients, comparing the algorithm results with the ground truth produced by the medical doctors. The Jaccard index between the algorithmic and the manual detection is 98.97%, with an overall accuracy of 99.62%.

PREDICTION OF VISUAL PROGNOSIS WITH SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY IN OUTER RETINAL ATROPHY SECONDARY TO CLOSED GLOBE TRAUMA

Retina ◽  
2013 ◽  
Vol 33 (6) ◽  
pp. 1258-1262 ◽  
Author(s):  
Haoyu Chen ◽  
Yufang Lu ◽  
Huichun Huang ◽  
Jianlong Zheng ◽  
Ping Hou ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Visual Prognosis ◽  
Retinal Atrophy

scholarly journals Automated Detection of Macular Hole in Optical Coherence Tomography Images using Depth-Check Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 105-108
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Hole ◽  
Automated Detection ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
Optical Coherence ◽  
Central Vision ◽  
Fiber Layer ◽  
Aged People ◽  
Depth Feature

Macular hole is a tear or break in the macula. It is located in the center of the retina and affects central vision of aged people. Optical Coherence Tomography (OCT) enables accurate diagnosis of macular hole. Existing algorithms available to detect cysts and retinal layers, but identifying macular hole in an accurate manner is still a missing entity. Hence we propose an automated system for the accurate macular hole detection. The proposed system has six stages in process. The first stage starts with preprocessing the OCT image, then detecting Nerve Fiber Layer (NFL). The detected NFL layer is then processed and depth feature is extracted. Then the macular hole is detected in OCT images using our proposed system. The proposed system is evaluated with the healthy macula and macular hole OCT images. The proposed system is also compared with other machine learning algorithms. By experimentation results, the proposed algorithm provides 94% accuracy in finding macular hole.

scholarly journals Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation

2016 ◽  
Vol 100 (10) ◽  
pp. 1372-1376 ◽  
Author(s):  
Ana-Maria Philip ◽  
Bianca S Gerendas ◽  
Li Zhang ◽  
Henrik Faatz ◽  
Dominika Podkowinski ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Choroidal Thickness ◽  
Ground Truth ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Swept Source
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