Ms. Yaning Wang Profile

Yaning Wang

at Johns Hopkins University

SPIE Involvement:

Author

Publications (10)

Proceedings Article | 13 March 2024 Presentation

Live rabbit eye model studies of optical coherence tomography guided pneumatic dissection using vertically inserted razor edge cannula for deep anterior lamellar keratoplasty

Yaning Wang, Justin Opfermann, James Kaluna, Ruizhi Zuo, Jesse Haworth, Axel Krieger, Bill Gensheimer, Jin Kang

Proceedings Volume PC12831, PC128310F (2024) https://doi.org/10.1117/12.3002995

KEYWORDS: Optical coherence tomography, Eye models, Cornea, Bubbles, Surgery, Robotics, In vivo imaging, Fiber optics sensors, Eye, Tissues

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Proceedings Article | 13 March 2024 Presentation + Paper

Deep learning-based single-shot fringe projection profilometry

Ruizhi Zuo, Shuwen Wei, Yaning Wang, Michael Kam, Justin Opfermann, Michael Hsieh, Axel Krieger, Jin Kang

Proceedings Volume 12831, 1283106 (2024) https://doi.org/10.1117/12.3001837

KEYWORDS: Depth maps, Point clouds, Biological samples, Fringe analysis, Cameras, Transformers, Reconstruction algorithms, Biological imaging, Tissues

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SPIE Journal Paper | 24 August 2023 Open Access

Depth-dependent attenuation and backscattering characterization of optical coherence tomography by stationary iterative method

Yaning Wang, Shuwen Wei, Jin U. Kang

JBO, Vol. 28, Issue 08, 085002, (August 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.8.085002

KEYWORDS: Signal attenuation, Optical coherence tomography, Backscatter, Tissues, Signal intensity, Monte Carlo methods, Biological samples, Scattering, Optical properties, Attenuation

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SignificanceExtracting optical properties of tissue [e.g., the attenuation coefficient (μ) and the backscattering fraction] from the optical coherence tomography (OCT) images is a valuable tool for parametric imaging and related diagnostic applications. Previous attenuation estimation models depend on the assumption of the uniformity of the backscattering fraction (R) within layers or whole samples, which does not accurately represent real-world conditions.AimOur aim is to develop a robust and accurate model that calculates depth-wise values of attenuation and backscattering fractions simultaneously from OCT signals. Furthermore, we aim to develop an attenuation compensation model for OCT images that utilizes the optical properties we obtained to improve the visual representation of tissues.ApproachUsing the stationary iteration method under suitable constraint conditions, we derived the approximated solutions of μ and R on a single scattering model. During the iteration, the estimated value of μ can be rectified by introducing the large variations of R, whereas the small ones were automatically ignored. Based on the calculation of the structure information, the OCT intensity with attenuation compensation was deduced and compared with the original OCT profiles.ResultsThe preliminary validation was performed in the OCT A-line simulation and Monte Carlo modeling, and the subsequent experiment was conducted on multi-layer silicone-dye-TiO2 phantoms and ex vivo cow eyes. Our method achieved robust and precise estimation of μ and R for both simulated and experimental data. Moreover, corresponding OCT images with attenuation compensation provided an improved resolution over the entire imaging range.ConclusionsOur proposed method was able to correct the estimation bias induced by the variations of R and provided accurate depth-resolved measurements of both μ and R simultaneously. The method does not require prior knowledge of the morphological information of tissue and represents more real-life tissues. Thus, it has the potential to help OCT imaging based disease diagnosis of complex and multi-layer biological tissue.

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Proceedings Article | 6 March 2023 Presentation + Paper

Common-path optical coherence tomography guided vertical pneumodissection for DALK

Yaning Wang, Shoujing Guo, Justin Opfermann, James Kaluna, Bill Gensheimer, Axel Krieger, Jin Kang

Proceedings Volume 12372, 1237204 (2023) https://doi.org/10.1117/12.2650533

KEYWORDS: Optical coherence tomography, Cornea, Tissues, Bubbles, Signal to noise ratio, Design and modelling, Surgery, Visualization, Sensors, Epoxies

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SPIE Journal Paper | 28 November 2022 Open Access

Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal optical imaging

Yaning Wang, Laura Tiusaba, Shimon Jacobs, Michele Saruwatari, Bo Ning, Marc Levitt, Anthony Sandler, So-Hyun Nam, Jin Kang, Jaepyeong Cha

JMI, Vol. 9, Issue 06, 064502, (November 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.6.064502

KEYWORDS: RGB color model, Laser speckle contrast imaging, Ischemia, Image registration, Surgery, Tissues, Data modeling, Education and training, Speckle, Gallium nitride

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PurposeIntraoperative evaluation of bowel perfusion is currently dependent upon subjective assessment. Thus, quantitative and objective methods of bowel viability in intestinal anastomosis are scarce. To address this clinical need, a conditional adversarial network is used to analyze the data from laser speckle contrast imaging (LSCI) paired with a visible-light camera to identify abnormal tissue perfusion regions.ApproachOur vision platform was based on a dual-modality bench-top imaging system with red-green-blue (RGB) and dye-free LSCI channels. Swine model studies were conducted to collect data on bowel mesenteric vascular structures with normal/abnormal microvascular perfusion to construct the control or experimental group. Subsequently, a deep-learning model based on a conditional generative adversarial network (cGAN) was utilized to perform dual-modality image alignment and learn the distribution of normal datasets for training. Thereafter, abnormal datasets were fed into the predictive model for testing. Ischemic bowel regions could be detected by monitoring the erroneous reconstruction from the latent space. The main advantage is that it is unsupervised and does not require subjective manual annotations. Compared with the conventional qualitative LSCI technique, it provides well-defined segmentation results for different levels of ischemia.ResultsWe demonstrated that our model could accurately segment the ischemic intestine images, with a Dice coefficient and accuracy of 90.77% and 93.06%, respectively, in 2560 RGB/LSCI image pairs. The ground truth was labeled by multiple and independent estimations, combining the surgeons’ annotations with fastest gradient descent in suspicious areas of vascular images. The total processing time was 0.05 s for an image size of 256 × 256.ConclusionsThe proposed cGAN can provide pixel-wise and dye-free quantitative analysis of intestinal perfusion, which is an ideal supplement to the traditional LSCI technique. It has potential to help surgeons increase the accuracy of intraoperative diagnosis and improve clinical outcomes of mesenteric ischemia and other gastrointestinal surgeries.

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Proceedings Article | 9 March 2022 Presentation

Numerical landmark detection algorithm for fringe projection profilometry during autonomous robotic suturing

Shuwen Wei, Michael Kam, Yaning Wang, Justin Opfermann, Hamed Saeidi, Michael Hsieh, Axel Krieger, Jin Kang

Proceedings Volume PC11949, PC119490A (2022) https://doi.org/10.1117/12.2609134

KEYWORDS: Robotics, Detection and tracking algorithms, Clouds, Tissues, Reliability, Luminescence, Safety, Robotic surgery, Robot vision, Light sources

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Proceedings Article | 7 March 2022 Presentation + Paper

3D motion-compensated optical coherence tomography based on higher-order regression towards real-time volumetric imaging of the cornea

Ruizhi Zuo, Yaning Wang, Kristina Irsch, Jin Kang

Proceedings Volume 11948, 1194805 (2022) https://doi.org/10.1117/12.2613155

KEYWORDS: Optical coherence tomography, Cornea, Tissues, 3D image processing, Imaging systems, Real time imaging, Natural surfaces, Image segmentation, Stereoscopy, Polarization

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Proceedings Article | 2 March 2022 Presentation + Paper

Depth-resolved backscattering signal reconstruction based OCT attenuation compensation

Yaning Wang, Shuwen Wei, Jin Kang

Proceedings Volume 11953, 119530D (2022) https://doi.org/10.1117/12.2609114

KEYWORDS: Signal attenuation, Backscatter, Optical coherence tomography, Biological research, Signal detection, Optical properties, Error analysis, Computer simulations, Tissue optics

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Proceedings Article | 2 March 2022 Presentation + Paper

Automated OCT A-line abdominal tissue classification using a hybrid MLP-CNN classifier during ventral hernia repair

Yaning Wang, Shuwen Wei, Justin Opfermann, Michael Kam, Hamed Saeidi, Michael Hsieh, Axel Krieger, Jin Kang

Proceedings Volume 11953, 1195307 (2022) https://doi.org/10.1117/12.2609103

KEYWORDS: Tissues, Optical coherence tomography, Signal attenuation, Surgery, Laparoscopy, Backscatter, Scattering, Sensors, Network architectures, Intestine

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Proceedings Article | 5 March 2021 Presentation + Paper

Optimized OCT-based depth-resolved model for attenuation compensation using point-spread-function calibration

Yaning Wang, Shuwen Wei, Shoujing Guo, Jin Kang

Proceedings Volume 11635, 116350E (2021) https://doi.org/10.1117/12.2577847

KEYWORDS: Signal attenuation, Optical coherence tomography, Tissues, Calibration, Monte Carlo methods, Image segmentation, Visualization, Speckle, Signal to noise ratio, Point spread functions

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