Safe and Fast Path Planner for Minimally Invasive Surgery

Nema, Shubhangi; Vachhani, Leena

doi:10.1109/iros51168.2021.9636268

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…To accomplish this, instruments used in a surgery need to be detected with multiple parts (segmenting surgical instruments in each consecutive frame for a complete surgical video) followed by tracking specific points on the instrument during surgery. The output can be used to code the actions 4 for a complete surgery that a learning or training platform can use 1,5 . Multi‐class segmentation of surgical instruments plays a vital role in facilitating instrument detection with a categorisation between the parts for enabling tracking or coding the actions.…”

Section: Introductionmentioning

confidence: 99%

Unpaired deep adversarial learning for multi‐class segmentation of instruments in robot‐assisted surgical videos

Nema

Vachhani

2023

Robotics Computer Surgery

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Purpose Image segmentation of instruments in the raw surgical videos is a critical component of intraoperative assistance softwares. Challenges include addressing rendered overlays occluding the instrument while providing pivotal input to instrument tracking frameworks and, train the segmentation process with limited labelled data available from surgical videos. Method The proposed adversarial network, InstruSegNet uses unpaired training (eliminating need for massive paired data) for automated multi‐class surgical instrument segmentation in raw surgical videos with complex backgrounds. The proposed method is applied for single/multiple robotic and rigid instruments and optimised on least square Generative Adversarial Networks loss. Result Promising validation has been conducted on the publicly available dataset. Proposed approach for multi‐class segmentation of robotic and rigid instruments meets outstanding performance in terms of accuracy and surpasses the existing methods. Application This work facilitates segmenting instrument information without manual interventions from raw videos providing means to code surgeon's actions for developing intelligent assistance software.

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Section: Introductionmentioning

confidence: 99%