Beta 1

Title 3D Image Guided Surgery Planning
Author Seliger, Andreas
Ipsen, Niels Bruun
Supervisor Paulsen, Rasmus Reinhold (Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Bachelor thesis
Year 2010
Abstract Surgical resection is the primary curative treatment for primary and secondary liver cancer. The estimation of tumor size and location in relation to other tissues is often done on a basis of 2D Computed Tomography slices. Software able to produce 3D surfaces of liver and tumor, based on CT slices, could be clinically beneficial when planning liver surgery. Reconstructing 3D surfaces from 2D data can be done in many different ways. A variety of segmentation algorithms, as well as surface reconstruction algorithms exist. This report makes use of a semi automatic segmentation algorithm based on a 2D geometric deformable model, to obtain point clouds from CT slices. Due to the high CT interslice distance, compared to the sampling density of the segmentation method used, the point clouds are anisotropic. Therefore they are pre-processed, in order to obtain more isotropically distributed data. This processing consists of downsampling and linear interpolation. After pre-processing, the data is introduced to the Sumatra toolkit, where implicit surface reconstruction is done, based on a prior energy function, either Difference of Laplacian or Membrane. The optimal combination of downsampling, interpolation and prior energy function, reviewed in a visual and “one-way” comparative fashion, was found to be factor two downsampling, two times interpolation and the Membrane prior energy model. The setup is used on a case study, with good results; reliable liver surface is produced and tumor localization is clarified. However, further tests of the used methods needs to be conducted and improvements of the algorithm can be done, in terms of automation, adding a prior knowledge and faster performance. A 3D geometric deformable model, sampling isotropically, with a choice of a prior knowledge that can be selected for the tissue/organ of interest and where the data can be pre-processed according to this selection, could provide several improvements. A high level of automation should be applied, but with some degree of expert interaction.
Note In cooperation with Rigshospitalet, MD, Linda Bardram, Surgery Division
Imprint Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark
Series IMM-B.Sc.-2010-11
Original PDF bac10_11.pdf (12.97 MB)
Admin Creation date: 2010-06-02    Update date: 2010-06-02    Source: dtu    ID: 262854    Original MXD