Automatic Detection and Segmentation of Robot-Assisted Surgical Motions

By Henry C. Lin, Izhak Shafran, T. E. Murphy, Allison M. Okamura, D. Yuh, Gregory Hager. 2005

104 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Kush R. Varshney
2. Homa Alemzadeh

In Computer Vision and Pattern Recognition

1. Kush R. Varshney
2. Homa Alemzadeh

In Surgery

1. Kush R. Varshney
2. Homa Alemzadeh

Citations in the corpus

• On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, methodology): “s knowledge into control and automation of surgical robots [37]. Machine learning techniques have been used for detection and classification of surgical motions for automated surgical skill evaluation [38]– [40] and automating portions of repetitive and time-consuming surgical tasks (e.g., knot-tying”
• A Chance-Constrained Programming Approach to Preoperative Planning of Robotic Cardiac Surgery Under Task-Level Uncertainty (2015, methodology): “Given the optimal plan P∗, M = 1000 pairs of target fixtures, denoted by xR ,i and xL,i , with Q∗x,R(k) and Q ∗ x,L(k) were randomly generated.”