TY - GEN
T1 - A Deep Regression Model for Seed Localization in Prostate Brachytherapy
AU - Yuan, Yading
AU - Sheu, Ren Dih
AU - Fu, Luke
AU - Lo, Yeh Chi
N1 - Funding Information:
Acknowledgment. This work is partially supported by grant UL1TR001433 from the National Center for Advancing Translational Sciences, National Institutes of Health, USA.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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 a very challenging task to identify these seeds in CT images due to the severe metal artifacts and high-overlapped appearance when multiple seeds are clustered together. In this paper, we propose an automatic and efficient algorithm based on a 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).
AB - 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 a very challenging task to identify these seeds in CT images due to the severe metal artifacts and high-overlapped appearance when multiple seeds are clustered together. In this paper, we propose an automatic and efficient algorithm based on a 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).
KW - 3D deep fully convolutional network
KW - Prostate brachytherapy
KW - Seed localization
UR - http://www.scopus.com/inward/record.url?scp=85075671033&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32254-0_43
DO - 10.1007/978-3-030-32254-0_43
M3 - Conference contribution
AN - SCOPUS:85075671033
SN - 9783030322533
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 385
EP - 393
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -