DaCartilageQuantification

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Image-based Quantification of Patella Cartilage using MRI - Evaluation of Novel Methods for Segmentation, Volume and Thickness Estimation

Thesis by: Shanshan Cui
Advisor: Prof. Nassir Navab, Priv.-Doz. Thomas M. Deserno
Supervision by: Dipl.-Inf. Ben Glocker
Due date: March 2008

Abstract

Osteoarthrosis (OA) is one of the major socio-economic burdens nowadays. There is a strong need for non-invasive, accurate, and efficient tools in order to support the clinical work of diagnosis and therapy. MRI technology provides superb soft tissue contrast and high resolution three-dimensional image data; MRI can visualize cartilage and other articular tissues directly. The combination of accurate image segmentation methods and tools for volume and thickness quantification are promising approaches which can deliver significant parameters for diagnosis and therapy. In the literature of cartilage segmentation, a perfect method for the patella cartilage segmentation has not been accomplished. In this project, we evaluate several different segmentation methods: atlas-based, shape models, semi-automatic and manual, to see the respective advantages and deficiencies. Then we suggest how to use different methods in appropriate situations.

We also present methods to calculate the volume of the cartilage instead of voxels numeration to increase the accuracy to the sub-voxels level. The reconstruction model accords with the anatomic shape of the patella cartilage. 3D visualization of the cartilage is made based on triangulated faces and minimum Euclidean distances from the vertexes of triangles of the cartilage bone interface to the triangles of the cartilage interface are calculated to find the minimum value at each point in order to determine the cartilage thickness distribution.

The proposed methods are tested and evaluated on a big number of data sets from healthy volunteers as well as patients suffering from OA. The cartilage volume and thickness of the pathologic and healthy data sets are analyzed to find the statistic characters and help the diagnosis in clinical work.

Resources

Literature


Students.ProjectForm
Title: Image-based Quantification of Patella Cartilage using MRI - Evaluation of Novel Methods for Segmentation, Volume and Thickness Estimation
Abstract: Osteoarthrosis (OA) is one of the major socio-economic burdens nowadays. There is a strong need for non-invasive, accurate, and efficient tools in order to support the clinical work of diagnosis and therapy. MRI technology provides superb soft tissue contrast and high resolution three-dimensional image data; MRI can visualize cartilage and other articular tissues directly. The combination of accurate image segmentation methods and tools for volume and thickness quantification are promising approaches which can deliver significant parameters for diagnosis and therapy. In the literature of cartilage segmentation, a perfect method for the patella cartilage segmentation has not been accomplished. In this project, we evaluate several different segmentation methods: atlas-based, shape models, semi-automatic and manual, to see the respective advantages and deficiencies. Then we suggest how to use different methods in appropriate situations. We also present methods to calculate the volume of the cartilage instead of voxels numeration to increase the accuracy to the sub-voxels level. The reconstruction model accords with the anatomic shape of the patella cartilage. 3D visualization of the cartilage is made based on triangulated faces and minimum Euclidean distances from the vertexes of triangles of the cartilage bone interface to the triangles of the cartilage interface are calculated to find the minimum value at each point in order to determine the cartilage thickness distribution. The proposed methods are tested and evaluated on a big number of data sets from healthy volunteers as well as patients suffering from OA. The cartilage volume and thickness of the pathologic and healthy data sets are analyzed to find the statistic characters and help the diagnosis in clinical work.
Student: Shanshan Cui
Director: Prof. Nassir Navab
Supervisor: Dipl.-Inf. Ben Glocker
Type: Master Thesis
Area: Medical Imaging
Status: finished
Start:  
Finish:  


Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 08 Jul 2008 - 19:34 - BenGlocker