MaSegmentationFracturedBones

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

Master thesis: Segmentation of Fractured Bones

Thesis by: Deepak Murali
Advisor: Nassir Navab
Supervision by: Dr. Abouzar Eslami, Dr. Jan Bauer, Dr. Peter Noël

Abstract

With the advent of computer aided surgery and planning, automatic post-processing of the acquired imaging data becomes more and more important but also challenging. Image segmentation is in many cases the first, very important but difficult step. A fully automatic method for segmenting bones would be highly desirable. However a few factors hamper the development of such a fully automated segmentation method. The quality of the datasets differs to a large extent in terms of contrast and resolution. The voxel intensity of the bones varies according to scan parameters and condition of the bone. The intensity of cortical and trabecular bone may be very similar in some cases. The density of osteoporotic bones is low and thus the contrast between osteoporotic bones and soft tissues is very small, in particular in fractured bones, where the trabecular bone directly adjoins to the soft tissues. In addition, it is very time consuming, inconsistent and hard to do a complete manual segmentation of the fractured bones. We look forward to propose a fast and efficient segmentation tool that effectively segments the fractures and at the same time is robust for using the output model in FEM analysis. Though the objective is to develop a segmentation tool that is as fully automated as possible, the idea is also to have the following features incorporated into the segmentation tool: semi-automatic segmentation, manual correction and output generation. The evaluation is done on CT datasets of various types of fractures in Department of Diagnostic and Interventional Radiology of Klinikum rechts der Isar in Munich.

ProjectForm
Title: Segmentation of Fractured Bones
Abstract: With the advent of computer aided surgery and planning, automatic post-processing of the acquired imaging data becomes more and more important but also challenging. Image segmentation is in many cases the first, very important but difficult step. A fully automatic method for segmenting bones would be highly desirable. However a few factors hamper the development of such a fully automated segmentation method. The quality of the datasets differs to a large extent in terms of contrast and resolution. The voxel intensity of the bones varies according to scan parameters and condition of the bone. The intensity of cortical and trabecular bone may be very similar in some cases. The density of osteoporotic bones is low and thus the contrast between osteoporotic bones and soft tissues is very small, in particular in fractured bones, where the trabecular bone directly adjoins to the soft tissues. In addition, it is very time consuming, inconsistent and hard to do a complete manual segmentation of the fractured bones. We look forward to propose a fast and efficient segmentation tool that effectively segments the fractures and at the same time is robust for using the output model in FEM analysis. Though the objective is to develop a segmentation tool that is as fully automated as possible, the idea is also to have the following features incorporated into the segmentation tool: semi-automatic segmentation, manual correction and output generation. The evaluation is done on CT datasets of various types of fractures in Department of Diagnostic and Interventional Radiology of Klinikum rechts der Isar in Munich.
Student: Deepak Murali
Director: Nassir Navab
Supervisor: Dr. Abouzar Eslami, Dr. Jan Bauer, Dr. Peter Noël
Type: Master Thesis
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Status: running
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