  Ultrasound-guided percutaneous needle insertion is a standard procedure
employed in both biopsy and ablation in clinical practices. However, due to the
complex interaction between tissue and instrument, the needle may deviate from
the in-plane view, resulting in a lack of close monitoring of the percutaneous
needle. To address this challenge, we introduce a robot-assisted ultrasound
(US) imaging system designed to seamlessly monitor the insertion process and
autonomously restore the visibility of the inserted instrument when
misalignment happens. To this end, the adversarial structure is presented to
encourage the generation of segmentation masks that align consistently with the
ground truth in high-order space. This study also systematically investigates
the effects on segmentation performance by exploring various training loss
functions and their combinations. When misalignment between the probe and the
percutaneous needle is detected, the robot is triggered to perform transverse
searching to optimize the positional and rotational adjustment to restore
needle visibility. The experimental results on ex-vivo porcine samples
demonstrate that the proposed method can precisely segment the percutaneous
needle (with a tip error of $0.37\pm0.29mm$ and an angle error of $1.19\pm
0.29^{\circ}$). Furthermore, the needle appearance can be successfully restored
under the repositioned probe pose in all 45 trials, with repositioning errors
of $1.51\pm0.95mm$ and $1.25\pm0.79^{\circ}$. from latex to text with math
symbols
