Query-by-example surgical activity detection

Purpose 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 c...

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Vydáno v:International journal for computer assisted radiology and surgery Ročník 11; číslo 6; s. 987 - 996
Hlavní autoři: Gao, Yixin, Vedula, S. Swaroop, Lee, Gyusung I., Lee, Mija R., Khudanpur, Sanjeev, Hager, Gregory D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
Témata:
Algorithms
Computer Imaging
Computer Science
Health Informatics
Humans
Imaging
Information Storage and Retrieval - methods
Medicine
Medicine & Public Health
Original Article
Pattern Recognition and Graphics
Radiology
Surgery
Surgical Procedures, Operative
Vision
Surgical activity detection
Query-by-example
Surgical data indexing
Stacked denoising autoencoder
Asymmetric subsequence dynamic time warping
ISSN:1861-6410, 1861-6429
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Shrnutí:Purpose 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. Methods 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. Results 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. Conclusion 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.
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ISSN:1861-6410
1861-6429
DOI:10.1007/s11548-016-1386-3