Deep learning segmentation of I-125 brachytherapy seeds in prostate cancer patients based on synthetically generated multi-echo training data

Motivation: Deep learning (DL) networks trained with synthetically generated data enable the visualization of I-125 brachytherapy seeds in prostate cancer patients in quantitative susceptibility mapping (QSM), possibly eliminating the need for a CT-scan in future. Goal(s): The Goal was to automatically detect and segment I-125 seeds in-vivo by using a DL network directly (without QSM) on gradient-echo-sequence (GRE) data. Approach: A U-Net was trained with synthetically generated multi-echo GRE magnitude and phase input data and corresponding target seed segmentations. Results: The seed segmentations were of high visual quality and showed good agreement (85% detection rate) with corresponding CT-scans in five prostate cancer patients. Impact: This work proposes a fast and completely automatic MRI-only based workflow for segmenting in-vivo brachytherapy seeds in prostate cancer patients. This approach has the potential to eliminate the need for a CT-scan, thereby reducing the use of ionizing radiation.