A Level-Set Approach to 3-D Segmentation of Lesions from T1-Weighted Spin-Echo DCE-MR ImagesStudent: Nikhil Rajguru |
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Active contours (or "snakes") are popularly used for segmentation of regions of
interest in medical images. The algorithm developed through this research
harnesses the properties of active contours in a level set approach to
perform the segmentation of high-resolution heterogeneous in-situ tumors from the
3-D dynamic contrast enhanced magnetic resonance images (DCE-MRI). The regions of
interest (ROIs) in this study were MDAmb231 tumor cells (the human breast
carcinoma cell line) grown in the mammary fat pads of severe combined
immuno-deficient (SCID) mice. This 3-D segmentation is a critical step towards
achieving a data fusion between the functional images derived from the MRI
acquisitions, and the reconstructed tumors in histology. Most
segmentation techniques for DCE-MRI are directed towards brain segmentation, and
were not well suited to the problem at hand. The edge sensitivity of the level-set
function to the boundary of the lesion was enhanced by combining forces derived
from the image including gradient vector flow. Since there were no standard
markers (as in brain segmentation) that could be used, tissue characteristics
could not be harnessed. Instead, the local probability near the lesion boundary was
utilized for level-set evolution. Based on experimental validation, the proposed
algorithm performed better than
other applicable segmentation techniques based on active contours on the data at
hand, and also the classical level-set approach.
Fig. 1: (a) Kass snake, (b) region growing + modified Kass, (c) GVF snake, (d) proposed algorithm. This work was a collaborative effort with Prof. Robert J. Gillies (Dept. of Biochemistry and Arizona Cancer Center). |