Related papers: Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation

Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation

URL: http://arxiv.org/abs/2304.09285v1
Date: Tue, 18 Apr 2023 20:48:14 GMT
Title: Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation
Authors: Benjamin D. Killeen, Han Zhang, Jan Mangulabnan, Mehran Armand, Russel H. Taylor, Greg Osgood, Mathias Unberath
Abstract summary: Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation. Using added supervision from detection of bony corridors, tools, and anatomy, we learn image representations that are fed into a transformer model to regress surgical phases. Our approach demonstrates the feasibility of X-ray-based SPR, achieving an average accuracy of 93.8% on simulated sequences and 67.57% in cadaver.
Score: 11.805185173756708
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity -- corridor, activity, view, and frame value -- simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data. Using added supervision from detection of bony corridors, tools, and anatomy, we learn image representations that are fed into a transformer model to regress surgical phases at the four granularity levels. Our approach demonstrates the feasibility of X-ray-based SPR, achieving an average accuracy of 93.8% on simulated sequences and 67.57% in cadaver across all granularity levels, with up to 88% accuracy for the target corridor in real data. This work constitutes the first step toward SPR for the X-ray domain, establishing an approach to categorizing phases in X-ray-guided surgery, simulating realistic image sequences to enable machine learning model development, and demonstrating that this approach is feasible for the analysis of real procedures. As X-ray-based SPR continues to mature, it will benefit procedures in orthopedic surgery, angiography, and interventional radiology by equipping intelligent surgical systems with situational awareness in the operating room.

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