PURPOSE: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. METHODS: In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during convolutional neural network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance runtime prediction. RESULTS: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. CONCLUSION: The analysis on tool imbalance, backed by the empirical results, indicates the need and superiority of the proposed framework over state-of-the-art techniques.
PURPOSE: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. METHODS: In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during convolutional neural network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance runtime prediction. RESULTS: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. CONCLUSION: The analysis on tool imbalance, backed by the empirical results, indicates the need and superiority of the proposed framework over state-of-the-art techniques.
Authors: Andru P Twinanda; Sherif Shehata; Didier Mutter; Jacques Marescaux; Michel de Mathelin; Nicolas Padoy Journal: IEEE Trans Med Imaging Date: 2016-07-22 Impact factor: 10.048
Authors: Alexander Lex; Nils Gehlenborg; Hendrik Strobelt; Romain Vuillemot; Hanspeter Pfister Journal: IEEE Trans Vis Comput Graph Date: 2014-12 Impact factor: 4.579
Authors: Xiaofei Du; Thomas Kurmann; Ping-Lin Chang; Maximilian Allan; Sebastien Ourselin; Raphael Sznitman; John D Kelly; Danail Stoyanov Journal: IEEE Trans Med Imaging Date: 2018-05 Impact factor: 10.048
Authors: Odysseas Zisimopoulos; Evangello Flouty; Mark Stacey; Sam Muscroft; Petros Giataganas; Jean Nehme; Andre Chow; Danail Stoyanov Journal: Healthc Technol Lett Date: 2017-09-14