A framework for the recognition of high-level surgical tasks from video images for cataract surgeries.

Florent Lalys 1, * Laurent Riffaud 2, 1 David Bouget 1 Pierre Jannin 1
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : The need for a better integration of the new generation of computer-assisted-surgical systems has been recently emphasized. One necessity to achieve this objective is to retrieve data from the operating room (OR) with different sensors, then to derive models from these data. Recently, the use of videos from cameras in the OR has demonstrated its efficiency. In this paper, we propose a framework to assist in the development of systems for the automatic recognition of high-level surgical tasks using microscope videos analysis. We validated its use on cataract procedures. The idea is to combine state-of-the-art computer vision techniques with time series analysis. The first step of the framework consisted in the definition of several visual cues for extracting semantic information, therefore, characterizing each frame of the video. Five different pieces of image-based classifiers were, therefore, implemented. A step of pupil segmentation was also applied for dedicated visual cue detection. Time series classification algorithms were then applied to model time-varying data. Dynamic time warping and hidden Markov models were tested. This association combined the advantages of all methods for better understanding of the problem. The framework was finally validated through various studies. Six binary visual cues were chosen along with 12 phases to detect, obtaining accuracies of 94%.
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Submitted on : Monday, February 13, 2012 - 4:20:02 PM
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Florent Lalys, Laurent Riffaud, David Bouget, Pierre Jannin. A framework for the recognition of high-level surgical tasks from video images for cataract surgeries.. IEEE Transactions on Biomedical Engineering, Institute of Electrical and Electronics Engineers, 2012, 59 (4), pp.966-76. ⟨10.1109/TBME.2011.2181168⟩. ⟨inserm-00669682⟩

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