Implicit active contours for automatic brachytherapy seed segmentation in fluoroscopy
Author(s) -
Eric M. Moult,
Clif Burdette,
Danny Y. Song,
Gábor Fichtinger,
Pascal Fallavollita
Publication year - 2012
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.911153
Subject(s) - centroid , brachytherapy , segmentation , computer science , artificial intelligence , ground truth , prostate brachytherapy , fluoroscopy , pixel , dosimetry , computer vision , image segmentation , visualization , pattern recognition (psychology) , nuclear medicine , medicine , radiation therapy , radiology
Motivation: In prostate brachytherapy, intra-operative dosimetry would be ideal to allow for rapid evaluation of the implant quality while the patient is still in the treatment position. Such a mechanism, however, requires 3-D visualization of the currently deposited seeds relative to the prostate. Thus, accurate, robust, and fully-automatic seed segmentation is of critical importance in achieving intra-operative dosimetry. Methodology: Implanted brachytherapy seeds are segmented by utilizing a region-based implicit active contour approach. Overlapping seed clusters are then resolved using a simple yet effective declustering technique. Results: Ground-truth seed coordinates were obtained via a published segmentation technique. A total of 248 clinical C-arm images from 16 patients were used to validate the proposed algorithm resulting in a 98.4% automatic detection rate with a corresponding 2.5% false-positive rate. The overall mean centroid error between the ground-truth and automatic segmentations was measured to be 0.42 pixels, while the mean centroid error for overlapping seed clusters alone was measured to be 0.67 pixels. Conclusion: Based on clinical data evaluation and validation, robust, accurate, and fully-automatic brachytherapy seed segmentation can be achieved through the implicit active contour framework and subsequent seed declustering method.
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