Slant Perception Under Stereomicroscopy

2017 ◽  
Vol 59 (7) ◽  
pp. 1128-1138 ◽  
Author(s):  
Samantha Horvath ◽  
Kori Macdonald ◽  
John Galeotti ◽  
Roberta L. Klatzky
Keyword(s):  
Optical Coherence Tomography ◽  
Augmented Reality ◽  
Human Perception ◽  
High Accuracy ◽  
Detection Task ◽  
Binocular Viewing ◽  
Optical Coherence ◽  
Slant Perception ◽  
Surface Slant ◽  
Planar Images

Objective These studies used threshold and slant-matching tasks to assess and quantitatively measure human perception of 3-D planar images viewed through a stereomicroscope. The results are intended for use in developing augmented-reality surgical aids. Background Substantial research demonstrates that slant perception is performed with high accuracy from monocular and binocular cues, but less research concerns the effects of magnification. Viewing through a microscope affects the utility of monocular and stereo slant cues, but its impact is as yet unknown. Method Participants performed in a threshold slant-detection task and matched the slant of a tool to a surface. Different stimuli and monocular versus binocular viewing conditions were implemented to isolate stereo cues alone, stereo with perspective cues, accommodation cue only, and cues intrinsic to optical-coherence-tomography images. Results At magnification of 5x, slant thresholds with stimuli providing stereo cues approximated those reported for direct viewing, about 12°. Most participants (75%) who passed a stereoacuity pretest could match a tool to the slant of a surface viewed with stereo at 5x magnification, with mean compressive error of about 20% for optimized surfaces. Slant matching to optical coherence tomography images of the cornea viewed under the microscope was also demonstrated. Conclusion Despite the distortions and cue loss introduced by viewing under the stereomicroscope, most participants were able to detect and interact with slanted surfaces. Application The experiments demonstrated sensitivity to surface slant that supports the development of augmented-reality systems to aid microscope-aided surgery.

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.

Full-Range Line-Field Optical Coherence Tomography for High-Accuracy Measurements of Optical Lens

2020 ◽  
Vol 69 (9) ◽  
pp. 7180-7190
Author(s):  
Bo Fang ◽  
Shuncong Zhong ◽  
Qiukun Zhang ◽  
Jianfeng Zhong ◽  
Qiben Lin ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Full Range ◽  
High Accuracy ◽  
Optical Coherence ◽  
Line Field ◽  
Optical Lens

scholarly journals High-accuracy endoscopic microscopy using a thin,1.5 mm diameter probe with optical coherence tomography

Synthesiology ◽  
2017 ◽  
Vol 11 (1) ◽  
pp. 23-32
Author(s):  
Hiromitsu FURUKAWA ◽  
Naomi NOGUCHI ◽  
Hiroshi YAMAZAKI ◽  
Takafumi ASADA
Keyword(s):  
Optical Coherence Tomography ◽  
High Accuracy ◽  
Optical Coherence

scholarly journals Accuracy of a Machine-Learning Algorithm for Detecting and Classifying Choroidal Neovascularization on Spectral-Domain Optical Coherence Tomography

2021 ◽  
Vol 11 (6) ◽  
pp. 524
Author(s):  
Andreas Maunz ◽  
Fethallah Benmansour ◽  
Yvonna Li ◽  
Thomas Albrecht ◽  
Yan-Ping Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Choroidal Neovascularization ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Age Related Macular Degeneration ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Age Related ◽  
Sd Oct

Background: To evaluate the performance of a machine-learning (ML) algorithm to detect and classify choroidal neovascularization (CNV), secondary to age-related macular degeneration (AMD) on spectral-domain optical coherence tomography (SD-OCT) images. Methods: Baseline fluorescein angiography (FA) and SD-OCT images from 1037 treatment-naive study eyes and 531 fellow eyes, without advanced AMD from the phase 3 HARBOR trial (NCT00891735), were used to develop, train, and cross-validate an ML pipeline combining deep-learning–based segmentation of SD-OCT B-scans and CNV classification, based on features derived from the segmentations, in a five-fold setting. FA classification of the CNV phenotypes from HARBOR was used for generating the ground truth for model development. SD-OCT scans from the phase 2 AVENUE trial (NCT02484690) were used to externally validate the ML model. Results: The ML algorithm discriminated CNV absence from CNV presence, with a very high accuracy (area under the receiver operating characteristic [AUROC] = 0.99), and classified occult versus predominantly classic CNV types, per FA assessment, with a high accuracy (AUROC = 0.91) on HARBOR SD-OCT images. Minimally classic CNV was discriminated with significantly lower performance. Occult and predominantly classic CNV types could be discriminated with AUROC = 0.88 on baseline SD-OCT images of 165 study eyes, with CNV from AVENUE. Conclusions: Our ML model was able to detect CNV presence and CNV subtypes on SD-OCT images with high accuracy in patients with neovascular AMD.

scholarly journals Volumetric 3D stitching of optical coherence tomography volumes

2018 ◽  
Vol 4 (1) ◽  
pp. 327-330 ◽  
Author(s):  
Max-Heinrich Laves ◽  
Lüder A. Kahrs ◽  
Tobias Ortmaier
Keyword(s):  
Optical Coherence Tomography ◽  
High Speed ◽  
Imaging Modality ◽  
Ground Truth ◽  
High Accuracy ◽  
Coordinate Frame ◽  
Robust Estimator ◽  
Optical Coherence ◽  
Volumetric Images ◽  
Panoramic Images

AbstractOptical coherence tomography (OCT) is a noninvasive medical imaging modality, which provides highresolution transectional images of biological tissue. However, its potential is limited due to a relatively small field of view. To overcome this drawback, we describe a scheme for fully automated stitching of multiple 3D OCT volumes for panoramic imaging. The voxel displacements between two adjacent images are calculated by extending the Lucas-Kanade optical flow a lgorithm to dense volumetric images. A RANSAC robust estimator is used to obtain rigid transformations out of the resulting flow v ectors. T he i mages a re t ransformed into the same coordinate frame and overlapping areas are blended. The accuracy of the proposed stitching scheme is evaluated on two datasets of 7 and 4 OCT volumes, respectively. By placing the specimens on a high-accuracy motorized translational stage, ground truth transformations are available. This results in a mean translational error between two adjacent volumes of 16.6 ± 0.8 μm (2.8 ± 0.13 voxels). To the author’s knowledge, this is the first reported stitching of multiple 3D OCT volumes by using dense voxel information in the registration process. The achieved results are sufficient for providing high accuracy OCT panoramic images. Combined with a recently available high-speed 4D OCT, our method enables interactive stitching of hand-guided acquisitions.

scholarly journals A high-accuracy and high-efficiency digital volume correlation method to characterize in-vivo optic nerve head biomechanics from optical coherence tomography

2021 ◽  
Author(s):  
Fuqiang Zhong ◽  
Junchao Wei ◽  
Yi Hua ◽  
Bo Wang ◽  
Juan Reynaud ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Gaussian Noise ◽  
High Efficiency ◽  
Speckle Noise ◽  
High Accuracy ◽  
Digital Volume Correlation ◽  
Optical Coherence

In-vivo optic nerve head (ONH) biomechanics characterization is emerging as a promising way to study eye physiology and pathology. We propose a high-accuracy and high-efficiency digital volume correlation (DVC) method for the purpose of characterizing the in-vivo ONH deformation from volumes acquired by optical coherence tomography (OCT). Using a combination of synthetic tests and analysis of OCTs from monkey ONHs subjected to acute and chronically elevated intraocular pressure, we demonstrate that our proposed methodology overcomes several challenges for conventional DVC methods. First, it accounts for large ONH rigid body motion in the OCT volumes which could otherwise lead to analysis failure; second, sub-voxel accuracy displacement can be guaranteed despite high noise and low image contrast of some OCT volumes; third, computational efficiency is greatly improved, such that the memory consumption of our method is substantially lower than with conventional methods; fourth, we introduce a parameter measuring displacements confidence. Test of image noise effects showed that the proposed DVC method had displacement errors smaller than 0.028 voxels with speckle noise and smaller than 0.037 voxels with Gaussian noise; The absolute (relative) strain errors in the three directions were lower than 0.0018 (4%) with speckle noise and than 0.0045 (8%) with Gaussian noise. Compared with conventional DVC methods, the proposed DVC method had substantially improved overall displacement and strain errors under large body motions (lower by up to 70%), with 75% lower computation times, while saving about 30% memory. The study thus demonstrates the potential of the proposed technique to investigate ONH biomechanics.

scholarly journals High-accuracy endoscopic microscopy using a thin, 1.5 mm diameter probe with optical coherence tomography

2018 ◽  
Vol 11 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Hiromitsu FURUKAWA ◽  
Naomi NOGUCHI ◽  
Hiroshi YAMAZAKI ◽  
Takafumi ASADA
Keyword(s):  
Optical Coherence Tomography ◽  
High Accuracy ◽  
Optical Coherence
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