Dr. Jing Wu Profile

Dr. Jing Wu

Research Scientist at Medizinische Univ Wien

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Author

Area of Expertise:

Medical Imaging , Coronary Artery Disease , Ohpthalmology , Cardiac CT , SD-OCT , Image Processing

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Publications (7)

Proceedings Article | 21 March 2016 Paper

Improve synthetic retinal OCT images with present of pathologies and textural information

Ehsan Shahrian Varnousfaderani, Wolf-Dieter Vogl, Jing Wu, Bianca Gerendas, Christian Simader, Georg Langs, Sebastian Waldstein, Ursula Schmidt-Erfurth

Proceedings Volume 9784, 97843V (2016) https://doi.org/10.1117/12.2217399

KEYWORDS: Optical coherence tomography, Retina, Image processing, Mathematical modeling, Denoising, Image enhancement, Speckle, Digital filtering

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Proceedings Article | 21 March 2016 Paper

Geodesic denoising for optical coherence tomography images

Ehsan Shahrian Varnousfaderani, Wolf-Dieter Vogl, Jing Wu, Bianca Gerendas, Christian Simader, Georg Langs, Sebastian Waldstein, Ursula Schmidt-Erfurth

Proceedings Volume 9784, 97840K (2016) https://doi.org/10.1117/12.2216972

KEYWORDS: Optical coherence tomography, Denoising, Image enhancement, Image resolution, 3D acquisition, 3D image processing, Ophthalmology, Diagnostics, Visualization, Image quality, Retina, Vitreous, Pathology, Speckle, Digital filtering

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Proceedings Article | 20 March 2015 Paper

Automated retinal fovea type distinction in spectral-domain optical coherence tomography of retinal vein occlusion

Jing Wu, Sebastian Waldstein, Bianca Gerendas, Georg Langs, Christian Simader, Ursula Schmidt-Erfurth

Proceedings Volume 9413, 94133D (2015) https://doi.org/10.1117/12.2076570

KEYWORDS: Optical coherence tomography, 3D image processing, Pathology, Retina, Veins, 3D scanning, Algorithm development, Image segmentation, Scanners, Medical imaging

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Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high- resolution, three-dimensional (3D) cross-sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD), glaucoma and retinal vein occlusion (RVO). Disease diagnosis, assessment, and treatment will require a patient to undergo multiple OCT scans, possibly using multiple scanners, to accurately and precisely gauge disease activity, progression and treatment success. However, cross-vendor imaging and patient movement may result in poor scan spatial correlation potentially leading to incorrect diagnosis or treatment analysis. The retinal fovea is the location of the highest visual acuity and is present in all patients, thus it is critical to vision and highly suitable for use as a primary landmark for cross-vendor/cross-patient registration for precise comparison of disease states. However, the location of the fovea in diseased eyes is extremely challenging to locate due to varying appearance and the presence of retinal layer destroying pathology. Thus categorising and detecting the fovea type is an important prior stage to automatically computing the fovea position.

Presented here is an automated cross-vendor method for fovea distinction in 3D SD-OCT scans of patients suffering from RVO, categorising scans into three distinct types. OCT scans are preprocessed by motion correction and noise filing followed by segmentation using a kernel graph-cut approach. A statistically derived mask is applied to the resulting scan creating an ROI around the probable fovea location from which the uppermost retinal surface is delineated. For a normal appearance retina, minimisation to zero thickness is computed using the top two retinal surfaces. 3D local minima detection and layer thickness analysis are used to differentiate between the remaining two fovea types. Validation employs ground truth fovea types identified by clinical experts at the Vienna Reading Center (VRC). The results presented here are intended to show the feasibility of this method for the accurate and reproducible distinction of retinal fovea types from multiple vendor 3D SD-OCT scans of patients suffering from RVO, and for use in fovea position detection systems as a landmark for intra- and cross-vendor 3D OCT registration.

Proceedings Article | 21 March 2014 Paper

Automated vessel shadow segmentation of fovea-centered spectral-domain images from multiple OCT devices

Jing Wu, Bianca Gerendas, Sebastian Waldstein, Christian Simader, Ursula Schmidt-Erfurth

Proceedings Volume 9034, 903403 (2014) https://doi.org/10.1117/12.2044034

KEYWORDS: Image segmentation, Optical coherence tomography, 3D image processing, Pathology, Image registration, 3D scanning, Retina, Image processing, Denoising, Scanners

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Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high reso- lution, three-dimensional (3D) cross sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD) and glaucoma.¹ Disease diagnosis, assessment, and treatment requires a patient to undergo multiple OCT scans, possibly using different scanning devices, to accurately and precisely gauge disease activity, progression and treatment success. However, the use of OCT imaging devices from different vendors, combined with patient movement may result in poor scan spatial correlation, potentially leading to incorrect patient diagnosis or treatment analysis. Image registration can be used to precisely compare disease states by registering differing 3D scans to one another. In order to align 3D scans from different time- points and vendors using registration, landmarks are required, the most obvious being the retinal vasculature. Presented here is a fully automated cross-vendor method to acquire retina vessel locations for OCT registration from fovea centred 3D SD-OCT scans based on vessel shadows. Noise filtered OCT scans are flattened based on vendor retinal layer segmentation, to extract the retinal pigment epithelium (RPE) layer of the retina. Voxel based layer profile analysis and k-means clustering is used to extract candidate vessel shadow regions from the RPE layer. In conjunction, the extracted RPE layers are combined to generate a projection image featuring all candidate vessel shadows. Image processing methods for vessel segmentation of the OCT constructed projection image are then applied to optimize the accuracy of OCT vessel shadow segmentation through the removal of false positive shadow regions such as those caused by exudates and cysts. Validation of segmented vessel shadows uses ground truth vessel shadow regions identified by expert graders at the Vienna Reading Center (VRC). The results presented here are intended to show the feasibility of this method for the accurate and precise extraction of suitable retinal vessel shadows from multiple vendor 3D SD-OCT scans for use in intra-vendor and cross-vendor 3D OCT registration, 2D fundus registration and actual retinal vessel segmentation. The resulting percentage of true vessel shadow segments to false positive segments identified by the proposed system compared to mean grader ground truth is 95%.

Proceedings Article | 23 February 2012 Paper

A fully automated multi-modal computer aided diagnosis approach to coronary calcium scoring of MSCT images

Jing Wu, Gordon Ferns, John Giles, Emma Lewis

Proceedings Volume 8315, 83152I (2012) https://doi.org/10.1117/12.904053

KEYWORDS: Calcium, Heart, Arteries, Image segmentation, Computer aided diagnosis and therapy, Computing systems, Lung, Blood circulation, Image processing, Computed tomography

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Proceedings Article | 14 February 2012 Paper

An automated multi-modal object analysis approach to coronary calcium scoring of adaptive heart isolated MSCT images

Jing Wu, Gordon Ferns, John Giles, Emma Lewis

Proceedings Volume 8314, 83142Y (2012) https://doi.org/10.1117/12.911518

KEYWORDS: Calcium, Heart, Image segmentation, Arteries, Blood circulation, Lung, Image processing, Tissues, Computed tomography, Liver

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Inter- and intra- observer variability is a problem often faced when an expert or observer is tasked with assessing the severity of a disease. This issue is keenly felt in coronary calcium scoring of patients suffering from atherosclerosis where in clinical practice, the observer must identify firstly the presence, followed by the location of candidate calcified plaques found within the coronary arteries that may prevent oxygenated blood flow to the heart muscle. This can be challenging for a human observer as it is difficult to differentiate calcified plaques that are located in the coronary arteries from those found in surrounding anatomy such as the mitral valve or pericardium. The inclusion or exclusion of false positive or true positive calcified plaques respectively will alter the patient calcium score incorrectly, thus leading to the possibility of incorrect treatment prescription. In addition to the benefits to scoring accuracy, the use of fast, low dose multi-slice CT imaging to perform the cardiac scan is capable of acquiring the entire heart within a single breath hold. Thus exposing the patient to lower radiation dose, which for a progressive disease such as atherosclerosis where multiple scans may be required, is beneficial to their health. Presented here is a fully automated method for calcium scoring using both the traditional Agatston method, as well as the Volume scoring method. Elimination of the unwanted regions of the cardiac image slices such as lungs, ribs, and vertebrae is carried out using adaptive heart isolation. Such regions cannot contain calcified plaques but can be of a similar intensity and their removal will aid detection. Removal of both the ascending and descending aortas, as they contain clinical insignificant plaques, is necessary before the final calcium scores are calculated and examined against ground truth scores of three averaged expert observer results. The results presented here are intended to show the requirement and feasibility for an automated scoring method that reduces the subjectivity and reproducibility error inherent with manual clinical calcium scoring.

Proceedings Article | 14 March 2011 Paper

A framework for automated coronary artery tracking of low axial resolution multi slice CT images

Jing Wu, Gordon Ferns, John Giles, Emma Lewis

Proceedings Volume 7962, 796234 (2011) https://doi.org/10.1117/12.877963

KEYWORDS: Arteries, Image segmentation, Heart, Image resolution, Computed tomography, Optical tracking, Visualization, Shape analysis, Calcium, Algorithm development

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Showing 5 of 7 publications

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