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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.

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Artikkelit ja abstraktit tieteellisissä konferenssijulkaisuissa © IEEE All rights reservedAbstract-Eye-hand coordination is a central skill in microsurgery. To develop efficacious microsurgical training environments to support development of eye-hand coordination, it is important to understand the workload associated with visuomotor tasks in microsurgery. We embedded an eye-tracker to a surgical microscope and collected eye-blink data of 10 participants during a microsuture training task. Blink-rate was shown to drop to low levels compared to a resting-state rate and be sensitive to the phases of microsurgical suture. We discuss these findings in the light of operator training in microsurgical environments.

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Imaging is an integral part of most operating room procedures and is used on a daily basis in the operating theater. Near real-time imaging during surgical procedures can significantly enhance the procedures by providing information about the anatomy and pathological conditions. So far, customizable spectral-imaging systems were only rarely investigated, developed and even less often installed in operational settings, especially microsurgery. In this paper we describe a design of portable imaging system for high throughput spectral characterization of ex vivo samples. The setup presented in this work was used for non-invasive collection of spectral signatures from a set of biological tissues.