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Augmenting Microsurgical Training: Microsurgical Instrument Detection Using Convolutional Neural Networks

In video-based training, clinicians practice and advance their skills on surgeries performed by their colleagues and themselves. Although microsurgeries are recorded daily, training centers are lacking the workforce to manually annotate the segments important for practitioners, such as instrument presence and position. In this work, we propose intelligent instrument detection using Convolutional Neural Network (CNN) to augment microsurgical training. The network was trained on real microsurgical practice videos for which human annotators manually gathered a large corpus of instrument positions. Under challenging conditions of highly magnified and often blurred view, the CNN was capable to correctly detect a needle-holder (a dominant tool in suturing practice) with 78.3% accuracy (F-score = 0.84) with recognition speed above 15 FPS. The result is promising in the emerging domain of augmented medical training where instrument recognition presents benefits to the microsurgical training.