DaTumorSegmentation

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

Master thesis: Tumor segmentation based on dynamic PET measurement

Advisor: Nassir Navab
Supervision by: Kuangyu Shi Ph.D.

Abstract

Modern cancer therapy has extended the frontline to tumor substructures. Molecular imaging makes it possible to identify underlying biological features of malicious lesions. As one important clinical imaging modality, positron emission tomography (PET) reveals the metabolic and pathological properties. Dynamic PET measurement has its potential to explore more information based on the kinetic difference of tumor microenvironment. This information could be used to assist cancer diagnosis, therapy planning and so on. After injection, radio labeled tracers experience different delivery, metabolism and clearance in Patients. The variant shapes of time activity curves (TAC), which are responses according time detected by PET, indicate different pathological characteristics of the targets. The goal of this project is to generate classification of substructures of solid tumors based on the quantitative and morphological features of TACs. Through trying different algorithms and criteria it is possible to have the clusters of different tumor features, such as hypoxia, necrosis and so on. The clusters could be validated through other imaging modalities and further with microscopic images.

Tasks

  • Creation of a flexible user interface for an already existing radiation therapy planning system on Qt basis
  • Validation of GUI in collaboration with medical doctors of the Radiation Therapy Department of Klinikum rechts der Isar

Requirements

  • A good knowledge of C++ is mandatory.
  • A good knowledge of statistical learning methods is necessary.
  • Knowledge of physiology and medical physics would be beneficial but is not mandatory.
  • Interest in medical applications is required and knowledge can be acquired during DA.

Contact

In case you are interested, please contact Kuangyu Shi (shi@lrz.tum.de) directly.

Klinik und Poliklinik für Strahlentherapie und Radiologische Onkologie
Klinikum rechts der Isar, Technische Universität München
Ismaninger Str. 22
D - 81675 München


ProjectForm
Title: Tumor segmentation based on dynamic PET measurement
Abstract: Modern cancer therapy has extended the frontline to tumor substructures. Molecular imaging makes it possible to identify underlying biological features of malicious lesions. As one important clinical imaging modality, positron emission tomography (PET) reveals the metabolic and pathological properties. Dynamic PET measurement has its potential to explore more information based on the kinetic difference of tumor microenvironment. This information could be used to assist cancer diagnosis, therapy planning and so on. After injection, radio labeled tracers experience different delivery, metabolism and clearance in Patients. The variant shapes of time activity curves (TAC), which are responses according time detected by PET, indicate different pathological characteristics of the targets. The goal of this project is to generate classification of substructures of solid tumors based on the quantitative and morphological features of TACs. Through trying different algorithms and criteria it is possible to have the clusters of different tumor features, such as hypoxia, necrosis and so on. The clusters could be validated through other imaging modalities and further with microscopic images.
Student:  
Director: Nassir Navab
Supervisor: Kuangyu Shi
Type: Master Thesis
Area:  
Status: open
Start:  
Finish:  


Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 21 Jul 2009 - 09:11 - StefanieDemirci