  Post-implant dosimetry (PID) is an essential step of prostate brachytherapy
that utilizes CT to image the prostate and allow the location and dose
distribution of the radioactive seeds to be directly related to the actual
prostate. However, it it a very challenging task to identify these seeds in CT
images due to the severe metal artifacts and high-overlapped appearance when
multiple seeds clustered together. In this paper, we propose an automatic and
efficient algorithm based on 3D deep fully convolutional network for
identifying implanted seeds in CT images. Our method models the seed
localization task as a supervised regression problem that projects the input CT
image to a map where each element represents the probability that the
corresponding input voxel belongs to a seed. This deep regression model
significantly suppresses image artifacts and makes the post-processing much
easier and more controllable. The proposed method is validated on a large
clinical database with 7820 seeds in 100 patients, in which 5534 seeds from 70
patients were used for model training and validation. Our method correctly
detected 2150 of 2286 (94.1%) seeds in the 30 testing patients, yielding 16%
improvement as compared to a widely-used commercial seed finder software
(VariSeed, Varian, Palo Alto, CA).
