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PURPOSE: Development of ultrasound-based tumor ablation monitoring systems requires extensive validation.
Validation is based on the comparison of ablated regions, computed from ultrasound images, to the ground truth region
observed on histopathology images. Registration of ultrasound and histopathology images can be efficiently
implemented by localizing fiducial lines embedded in the test phantom. Manual fiducial localization is time consuming
and may be inaccurate. Current automatic localization algorithms were designed for use on images containing easily
detectable fiducials in clear water, while the images produced by the ablation monitoring platform contain fiducials and
ablated tissue embedded in tissue-mimicking gel. Our goal was to develop an automatic fiducial localization algorithm
for the ablation monitoring platform. METHOD: A previously existing algorithm for detecting fishing line in water for
ultrasound probe calibration, created by Chen et al., was tested on ultrasound images of an ablation phantom. Fiducial
and line point detection parameters were determined by running the algorithm multiple times with different parameter
sets and searching for the set that results in the best detection success rate. The fiducial intensity scoring method was
modified to use intensities from an unaltered image; this greatly reduced the number of incorrectly identified fiducials.
Line finding was modified to suit the ablation phantom geometry. RESULTS: The new algorithm was tested by
comparing the automatic localization results to manually identified fiducial positions. Using the optimized parameters, it
was found to have a 94.1 % success rate on the tested images. Fiducial localization error was defined as the difference
between the manually segmented positions and the positions found by the algorithm. Fiducial localization error was -
0.04±0.18mm along the x-axis, and -0.09±0.14mm along the y-axis. CONCLUSION: We have developed an automatic
algorithm that detects line fiducials at a high success rate in complex phantoms containing a tissue sample embedded in
tissue-mimicking gel.
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