Project

Airway Segmentation from Chest CT Images

Segmentation of human airways from chest CT images is an important and difficult task. A precise and accurate segmentation is a prerequisite for further analysis in diagnosis and treatment of airway related diseases, such as chronic obstructive pulmonary disease . However, the low quality of acquired images, the branching structures, as well as pathologies make the task challenging. The database utilized in this study is mainly from the open challenge EXACT09.

My research on airway segmentation include the following conventional methods:

Region growing-based algorithm
Frangi tubeness algorithm
Circle fitting detector algorithm
Gradient vector flow-based algorithm
Cavity enhancement filtering-based algorithm

As well as the following deep learning-based methods:

3-D Frangi-Net
3-D U-Net and its variants

Retinal Vessel Segmentation from Fundus Images

Retinal vessel segmentation is a crucial step in fundus image analysis. It provides information of the distribution, thickness and curvature of the retinal vessels, thus greatly assists early stage diagnosis of circulate system related diseases, such as diabetic retinopathy.

Manual annotation is tedious and time consuming, thus automatic segmentation algorithms are investigated on. My research cover the following topics:

Lacunae and Blood Vessel Segmentation from Mouse Bone XRM Images

This research topic is part of the project 4D+nanoSCOPE: Advancing osteoporosis medicine by observing bone microstructure and remodelling using a four-dimensional nanoscope.

The project is funded by European Research Council (ERC) with € 12,366,635 million for 72 months. It is jointly proposed by Prof. Dr. Georg Schett, Director of the Department of Medicine 3, Universitätsklinikum Erlangen, Prof. Dr. Andreas Maier from the Department of Computer Science 5 at FAU, and Prof. Dr. Silke Christiansen from Fraunhofer Institute for Ceramic Technologies and Systems IKT.

Contact

Address

Weilin Fu M. Sc.

Researcher in the Image Analysis (IMA) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg



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Department of Computer Science 5 Our Team Fu, Weilin Project Publications Thesis Curriculum Vitae Research Publications Free Software Data Courses Curriculum Theses Press Releases Cooperations Open Positions LME Videos Ph.D. Gallery Contact Intranet Impressum Datenschutzerklärung Contact +49 9131 85 25246 +49 9131 85 27270 09.153 Address Universität Erlangen-Nürnberg Chair of Computer Science 5 (Pattern Recognition) Martensstr. 3 91058 Erlangen Germany Driving directions Powered by	Dept. of Computer Sc. » Pattern Recognition » Our Team » Fu, Weilin » Project Weilin Fu M. Sc. Researcher in the Image Analysis (IMA) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg Weilin Fu Airway Segmentation from Chest CT Images Segmentation of human airways from chest CT images is an important and difficult task. A precise and accurate segmentation is a prerequisite for further analysis in diagnosis and treatment of airway related diseases, such as chronic obstructive pulmonary disease . However, the low quality of acquired images, the branching structures, as well as pathologies make the task challenging. The database utilized in this study is mainly from the open challenge EXACT09. My research on airway segmentation include the following conventional methods: Region growing-based algorithm Frangi tubeness algorithm Circle fitting detector algorithm Gradient vector flow-based algorithm Cavity enhancement filtering-based algorithm As well as the following deep learning-based methods: 3-D Frangi-Net 3-D U-Net and its variants Retinal Vessel Segmentation from Fundus Images Retinal vessel segmentation is a crucial step in fundus image analysis. It provides information of the distribution, thickness and curvature of the retinal vessels, thus greatly assists early stage diagnosis of circulate system related diseases, such as diabetic retinopathy. Manual annotation is tedious and time consuming, thus automatic segmentation algorithms are investigated on. My research cover the following topics: 2-D Frangi-Net U-Net variants Interpretable network pipeline Transfer learning from fundus data to OCTA data Lacunae and Blood Vessel Segmentation from Mouse Bone XRM Images This research topic is part of the project 4D+nanoSCOPE: Advancing osteoporosis medicine by observing bone microstructure and remodelling using a four-dimensional nanoscope. The project is funded by European Research Council (ERC) with € 12,366,635 million for 72 months. It is jointly proposed by Prof. Dr. Georg Schett, Director of the Department of Medicine 3, Universitätsklinikum Erlangen, Prof. Dr. Andreas Maier from the Department of Computer Science 5 at FAU, and Prof. Dr. Silke Christiansen from Fraunhofer Institute for Ceramic Technologies and Systems IKT.