  Ultrasound image quality has continually been improving. However, when
needles or other metallic objects are operating inside the tissue, the
resulting reverberation artifacts can severely corrupt the surrounding image
quality. Such effects are challenging for existing computer vision algorithms
for medical image analysis. Needle reverberation artifacts can be hard to
identify at times and affect various pixel values to different degrees. The
boundaries of such artifacts are ambiguous, leading to disagreement among human
experts labeling the artifacts. We propose a weakly- and semi-supervised,
probabilistic needle-and-reverberation-artifact segmentation algorithm to
separate the desired tissue-based pixel values from the superimposed artifacts.
Our method models the intensity decay of artifact intensities and is designed
to minimize the human labeling error. We demonstrate the applicability of the
approach and compare it against other segmentation algorithms. Our method is
capable of differentiating between the reverberations from artifact-free
patches as well as of modeling the intensity fall-off in the artifacts. Our
method matches state-of-the-art artifact segmentation performance and sets a
new standard in estimating the per-pixel contributions of artifact vs
underlying anatomy, especially in the immediately adjacent regions between
reverberation lines. Our algorithm is also able to improve the performance
downstream image analysis algorithms.
