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Post-implant dosimetry (PID) is an essential step of prostate brachytherapythat utilizes CT to image the prostate and allow the location and dosedistribution of the radioactive seeds to be directly related to the actualprostate. However, it it a very challenging task to identify these seeds in CTimages due to the severe metal artifacts and high-overlapped appearance whenmultiple seeds clustered together. In this paper, we propose an automatic andefficient algorithm based on 3D deep fully convolutional network foridentifying implanted seeds in CT images. Our method models the seedlocalization task as a supervised regression problem that projects the input CTimage to a map where each element represents the probability that thecorresponding input voxel belongs to a seed. This deep regression modelsignificantly suppresses image artifacts and makes the post-processing mucheasier and more controllable. The proposed method is validated on a largeclinical database with 7820 seeds in 100 patients, in which 5534 seeds from 70patients were used for model training and validation. Our method correctlydetected 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).

A Deep Regression Model for Seed Identification in Prostate Brachytherapy