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 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 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.

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%.

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

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.

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
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