Dr. Chunliang Wang Profile

Dr. Chunliang Wang

Researcher at KTH Royal Institute of Technology

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Publications (11)

SPIE Journal Paper | 9 July 2024 Open Access

Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation

Simone Bendazzoli, Emelie Bäcklin, Örjan Smedby, Birgitta Janerot-Sjoberg, Bryan Connolly, Chunliang Wang

JMI, Vol. 11, Issue 04, 044001, (July 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.4.044001

KEYWORDS: Lung, Image segmentation, Anatomy, Education and training, COVID 19, Prior knowledge, Voxels, Performance modeling, Computed tomography, Network architectures

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Proceedings Article | 4 March 2022 Poster + Presentation + Paper

Estimation of the cross-sectional surface area of the waist of the nerve fiber layer at the optic nerve head

Konstancija Kisonaite, Zhaohua Yu, Faisal Raeme, Simone Bendazzoli, Chunliang Wang, Per Söderberg

Proceedings Volume 11941, 119410F (2022) https://doi.org/10.1117/12.2608073

KEYWORDS: Nerve, Optic nerve, Head, Evolutionary algorithms, Retina, Optical coherence tomography, Axons, Artificial intelligence, Visualization, Image segmentation

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Proceedings Article | 5 March 2021 Presentation + Paper

Fully automatic estimation of the waist of the nerve fiber layer at the optic nerve head angularly resolved

Irene Brusini, Gabriel Carizzo, Simone Bendazzoli, Chunliang Wang, Zhaohua Yu, Camilla Sandberg Melin, Konstancija Kisonaite, Faisal Raeme, Per Söderberg

Proceedings Volume 11623, 116231D (2021) https://doi.org/10.1117/12.2583562

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The present project aims at developing a fully automatic software for estimation of the waist of the nerve fiber layer in the Optic Nerve Head (ONH) angularly resolved in the frontal plane as a tool for morphometric monitoring of glaucoma. The waist of the nerve fiber layer is here defined as Pigment epithelium central limit –Inner limit of the retina – Minimal Distance, (PIMD). 3D representations of the ONH were collected with high resolution OCT in young not glaucomatous eyes and glaucomatous eyes. An improved tool for manual annotation was developed in Python. This tool was found user friendly and to provide sufficiently precise manual annotation. PIMD was automatically estimated with a software consisting of one AI model for detection of the inner limit of the retina and another AI model for localization of the Optic nerve head Pigment epithelium Central limit (OPCL). In the current project, the AI model for OPCL localization was retrained with new data manually annotated with the improved tool for manual annotation both in not glaucomatous eyes and in glaucomatous eyes. Finally, automatic annotations were compared to 3 annotations made by 3 independent annotators in an independent subset of both the not glaucomatous and the glaucomatous eyes. It was found that the fully automatic estimation of PIMD-angle overlapped the 3 manual annotators with small variation among the manual annotators. Considering interobserver variation, the improved tool for manual annotation provided less variation than our original annotation tool in not glaucomatous eyes suggesting that variation in glaucomatous eyes is due to variable pathological anatomy, difficult to annotate without error. The small relative variation in relation to the substantial overall loss of PIMD in the glaucomatous eyes compared to the not glaucomatous eyes suggests that our software for fully automatic estimation of PIMD-angle can now be implemented clinically for monitoring of glaucoma progression.

Proceedings Article | 19 February 2020 Paper

Fully automatic estimation of the angular distribution of the waist of the nerve fiber layer in the optic nerve head

Gabriel Carrizo, Zhaohua Yu, Chunliang Wang, Camilla Sandberg Melin, Per Söderberg, Konstancia Kisonaite

Proceedings Volume 11218, 112180I (2020) https://doi.org/10.1117/12.2554445

KEYWORDS: Convolutional neural networks, Artificial neural networks, Machine learning, Ophthalmology, Image segmentation, Optical coherence tomography, Optic nerve, Retina, Neural networks, Eye

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Proceedings Article | 15 March 2019 Paper

Automatic rat brain segmentation from MRI using statistical shape models and random forest

Simone Bendazzoli, Irene Brusini, Peter Damberg, Örjan Smedby, Leif Andersson, Chunliang Wang

Proceedings Volume 10949, 109492O (2019) https://doi.org/10.1117/12.2512409

KEYWORDS: Machine learning, Principal component analysis, Brain imaging, Image segmentation, Brain, Neuroimaging, Data modeling, Magnetic resonance imaging, Statistical modeling

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Proceedings Article | 15 March 2019 Presentation + Paper

Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution

Fengkai Wan, Örjan Smedby, Chunliang Wang

Proceedings Volume 10949, 1094909 (2019) https://doi.org/10.1117/12.2512950

KEYWORDS: Convolutional neural networks, Image segmentation, Magnetic resonance imaging, Bone, 3D image processing, Cartilage, Image processing algorithms and systems, Image filtering

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Proceedings Article | 24 February 2017 Paper

Segmentation of cortical bone using fast level sets

Manish Chowdhury, Daniel Jörgens, Chunliang Wang, Örjan Smedby, Rodrigo Moreno

Proceedings Volume 10133, 1013327 (2017) https://doi.org/10.1117/12.2254240

KEYWORDS: Image segmentation, Bone, Image analysis, Medical imaging, Image resolution, Computed tomography, Biological research, 3D image processing, Visualization, Optical spheres, Surgery, Binary data

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Proceedings Article | 18 March 2016 Paper

Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain

Filipe M. Marreiros, Chunliang Wang, Sandro Rossitti, Örjan Smedby

Proceedings Volume 9786, 978611 (2016) https://doi.org/10.1117/12.2208421

KEYWORDS: Brain, Magnetic resonance angiography, Distance measurement, Error analysis, Image registration, 3D image processing, Surgery, Image segmentation, Medical imaging, Veins, Neuroimaging

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SPIE Journal Paper | 15 February 2016 Open Access

Superficial vessel reconstruction with a Multiview camera system

Filipe M. Marreiros, Sandro Rossitti, Per Karlsson, Chunliang Wang, Torbjörn Gustafsson, Per Carleberg, Örjan Smedby

JMI, Vol. 3, Issue 01, 019801, (February 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.1.019801

KEYWORDS: Cameras, Imaging systems, Medical imaging, Information technology, Image visualization, Visualization, Health sciences, MATLAB

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SPIE Journal Paper | 5 January 2016

Superficial vessel reconstruction with a multiview camera system

Filipe Marreiros, Sandro Rossitti, Per Karlsson, Chunliang Wang, Torbjörn Gustafsson, Per Carleberg, Örjan Smedby

JMI, Vol. 3, Issue 01, 015001, (January 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.1.015001

KEYWORDS: Optical spheres, Cameras, Brain, Imaging systems, Neuroimaging, Spatial coherence, Magnetic resonance imaging, Calibration, Computed tomography, Computing systems

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Proceedings Article | 12 March 2011 Paper

Level set based vessel segmentation accelerated with periodic monotonic speed function

Chunliang Wang, Hans Frimmel, Örjan Smedby

Proceedings Volume 7962, 79621M (2011) https://doi.org/10.1117/12.876704

KEYWORDS: Image segmentation, Medical imaging, Image processing algorithms and systems, Partial differential equations, Detection and tracking algorithms, Radiology, Image visualization, Visualization, Information technology, 3D metrology

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Showing 5 of 11 publications

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