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.