Simulation of brain tumors in MR images for evaluation of segmentation efficacy.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2660387/
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Links to Media http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2660387/
Publication Type journal article
Creator Gerig, Guido
Other Author Prastawa, Marcel; Bullitt, Elizabeth
Title Simulation of brain tumors in MR images for evaluation of segmentation efficacy.
Date 2009-01-01
Description Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multimodal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST (Response Evaluation Criteria in Solid Tumors) criteria.
Type InteractiveResource
Publisher Elsevier
Journal Title Medical Image Analysis
Volume 13
Issue 2
First Page 297
Last Page 311
DOI doi.org/10.1016/j.media.2008.11.002
Subject Brain MRI; Segmentation validation; Tumor simulation; Simulation of tumor infiltration; Diffusion tensor imaging; Ground truth; Gold standard
Language eng
Bibliographic Citation Prastawa, M., Bullitt, E., & Gerig, G. (2009). Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Medical Image Analysis, 13(2), 297-311.
Rights Management (c) Elsevier ; Authors manuscript from Prastawa, M. W., Bullitt, E., & Gerig, G. (2009). Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Medical Image Analysis, 13(2), 297-311. http://dx.doi.org/10.1016/j.media.2008.11.002.
Format Medium application/html
Identifier uspace, 19227
ARK ark:/87278/s6r530wv
Setname ir_uspace
Date Created 2015-03-09
Date Modified 2015-03-09
ID 712868
Reference URL https://collections.lib.utah.edu/ark:/87278/s6r530wv
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