Query-by-example surgical activity detection
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Easy acquisition of surgical data opens many opportunities to automate skill evaluation and teaching. Current technology to search tool motion data for surgical activity segments of interest is limited by the need for manual pre-processing, which can be prohibitive at scale. We developed a content-based information retrieval method, query-by-example (QBE), to automatically detect activity segments within surgical data recordings of long duration that match a query.
The example segment of interest (query) and the surgical data recording (target trial) are time series of kinematics. Our approach includes an unsupervised feature learning module using a stacked denoising autoencoder (SDAE), two scoring modules based on asymmetric subsequence dynamic time warping (AS-DTW) and template matching, respectively, and a detection module. A distance matrix of the query against the trial is computed using the SDAE features, followed by AS-DTW combined with template scoring, to generate a ranked list of candidate subsequences (substrings). To evaluate the quality of the ranked list against the ground-truth, thresholding conventional DTW distances and bipartite matching are applied. We computed the recall, precision, F1-score, and a Jaccard index-based score on three experimental setups. We evaluated our QBE method using a suture throw maneuver as the query, on two tool motion datasets (JIGSAWS and MISTIC-SL) captured in a training laboratory.
We observed a recall of 93, 90 and 87 % and a precision of 93, 91, and 88 % with same surgeon same trial (SSST), same surgeon different trial (SSDT) and different surgeon (DS) experiment setups on JIGSAWS, and a recall of 87, 81 and 75 % and a precision of 72, 61, and 53 % with SSST, SSDT and DS experiment setups on MISTIC-SL, respectively.
We developed a novel, content-based information retrieval method to automatically detect multiple instances of an activity within long surgical recordings. Our method demonstrated adequate recall across different complexity datasets and experimental conditions.
KeywordsQuery-by-example Stacked denoising autoencoder Asymmetric subsequence dynamic time warping Surgical data indexing Surgical activity detection
We acknowledge Intuitive Surgical Inc., Sunnyvale, CA for facilitating capture of data from the da Vinci Surgical Systems for the JIGSAWS and MISTIC-SL datasets. We would also like to thank Anand Malpani and Madeleine Waldram for the MISTIC-SL dataset collection and processing.
Compliance with ethical standards
Conflicts of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
The Johns Hopkins Science of Learning Institute provided a research grant to conduct the study that yielded the MISTIC-SL dataset. Y. Gao was supported by Department of Computer Science, The Johns Hopkins University.
Informed consent was obtained from all individual participants included in the MISTIC-SL study. The JIGSAWS dataset is publicly accessible.
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