Open AccessAutomatic detection and tracking of marker seeds implanted in prostate cancer patients using a deep learning algorithm
Author(s) -
Keya Amarsee,
R Prabhakar,
Andrew Fielding,
Margot Lehman,
Christopher Noble,
Ben Perrett,
Daryl Ning
Publication year - 2021
Publication title -
journal of medical physics/journal of medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.292
H-Index - 24
eISSN - 1998-3913
pISSN - 0971-6203
DOI - 10.4103/jmp.jmp_117_20
Subject(s) - fiducial marker , ground truth , artificial intelligence , computer science , projection (relational algebra) , volume (thermodynamics) , tracking (education) , prostate cancer , computer vision , hough transform , imaging phantom , matlab , nuclear medicine , medicine , algorithm , image (mathematics) , cancer , physics , psychology , pedagogy , quantum mechanics , operating system
Fiducial marker seeds are often used as a surrogate to identify and track the positioning of prostate volume in the treatment of prostate cancer. Tracking the movement of prostate seeds aids in minimizing the prescription dose spillage outside the target volume to reduce normal tissue complications. In this study, You Only Look Once (YOLO) v2™ (MathWorks™) convolutional neural network was employed to train ground truth datasets and develop a program in MATLAB that can visualize and detect the seeds on projection images obtained from kilovoltage (kV) X-ray volume imaging (XVI) panel (Elekta™).