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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| Vydané v: | International journal for computer assisted radiology and surgery Ročník 11; číslo 6; s. 987 - 996 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2016
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| ISSN: | 1861-6410, 1861-6429 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. PURPOSEEasy 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.METHODSThe 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.RESULTSWe 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.CONCLUSIONWe 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. |
| Author | Lee, Mija R. Lee, Gyusung I. Hager, Gregory D. Vedula, S. Swaroop Gao, Yixin Khudanpur, Sanjeev |
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| Cites_doi | 10.1109/ICRA.2016.7487608 10.1007/978-3-642-40760-4_43 10.21437/Interspeech.2011-304 10.1007/978-3-540-74048-3_4 10.1016/j.media.2013.04.007 10.1007/978-3-642-40811-3_4 10.1016/j.jsurg.2015.11.009 10.1145/1390156.1390294 10.1109/TASSP.1978.1163055 10.1007/978-3-642-30618-1_17 10.1109/ICRA.2016.7487305 10.1007/s11548-015-1238-6 10.1007/978-3-642-04268-3_53 10.1109/ASRU.2009.5372889 10.1007/978-3-319-10443-0_52 10.1007/978-3-642-33415-3_5 10.1109/WACV.2015.154 |
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| Keywords | Surgical activity detection Query-by-example Surgical data indexing Stacked denoising autoencoder Asymmetric subsequence dynamic time warping |
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| PublicationSubtitle | A journal for interdisciplinary research, development and applications of image guided diagnosis and therapy |
| PublicationTitle | International journal for computer assisted radiology and surgery |
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| References | Béjar B, Zappella L, Vidal R (2012) Surgical gesture classification from video data. In: Medical image computing and computer-assisted intervention—MICCAI 2012. Springer, Nice, France, pp 34–41 Carlin M, Thomas S, Jansen A, Hermansky H (2011) Rapid evaluation of speech representations for spoken term discovery. In: Proceedings of the annual conference of the international speech communication association, INTERSPEECH, pp 821–824 Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Bejar B, Yuh DD, Chen CCG, Vidal R, Khudanpur S, Hager GD (2014) The JHU-ISI gesture and skill assessment dataset (JIGSAWS): a surgical activity working set for human motion modeling. In: Medical image computing and computer-assisted intervention M2CAI—MICCAI workshop Ahmidi N, Gao Y, Béjar B, Vedula SS, Khudanpur S, Vidal R, Hager GD (2013) String motif-based description of tool motion for detecting skill and gestures in robotic surgery. In: Medical image computing and computer-assisted intervention–MICCAI 2013. Springer, Nagoya, Japan Tao L, Zappella L, Hager GD, Vidal R (2013) Surgical gesture segmentation and recognition. In: Medical image computing and computer-assisted intervention—MICCAI 2013, Nagoya, Japan Lea C, Hager GD, Vidal R (2015) An improved model for segmentation and recognition of fine-grained activities with application to surgical training tasks. In: 2015 IEEE Winter Conference on applications of computer vision (WACV), pp 1123–1129 Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning—ICML ’08, pp 1096–1103 Hazen T, Shen W, White C (2009) Query-by-example spoken term detection using phonetic posteriorgram templates. In: IEEE workshop on automatic speech recognition understanding, 2009. ASRU 2009, pp 421–426 Lea C, Vidal R, Hager GD (2016) Learning convolutional action primitives from multimodal timeseries data. In: Proceedings of the IEEE international conference on robotics and automation—ICRA 2016 (Accepted) Gao Y, Vedula SS, Lee GI, Lee MR, Khudanpur S, Hager GD (2016) Unsupervised surgical data alignment with application to automatic activity annotation. In: Proceedings of the IEEE international conference on robotics and automation—ICRA 2016 (Accepted) BengioYLamblinPPopoviciDLarochelleHGreedy layer-wise training of deep networksAdv Neural Inf Process Syst200719153160 Sefati S, Cowan NJ, Vidal R (2015) Learning shared, discriminative dictionaries for surgical gesture segmentation and classification. In: Modeling and monitoring of computer assisted interventions (M2CAI)—MICCAI workshop Vedula SS, Malpani A, Ahmidi N, Khudanpur S, Hager G, Chen CCG (2016) Task-level vs. segment-level quantitative metrics for surgical skill assessment. J Surg Educ 73(2). doi:10.1016/j.jsurg.2015.11.009 Muller M (2007) Dynamic time warping. In: Information retrieval for music and motion. Springer, New York Twinanda AP, de Mathelin M, Padoy N (2014) Fisher kernel based task boundary retrieval in laparoscopic database with single video query. In: Medical image computing and computer-assisted intervention—MICCAI 2014, Boston, MA SakoeHChibaSDynamic programming algorithm optimization for spoken word recognitionIEEE Trans Acoust Speech Signal Process1978261434910.1109/TASSP.1978.1163055 ZappellaLBéjarBHagerGDVidalRSurgical gesture classification from video and kinematic dataMed Image Anal20131773274510.1016/j.media.2013.04.00723706754 Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R (2012) Sparsehidden markov models for surgical gesture classification and skill evaluation. In: Information processing in computer-assisted interventions. Springer, Berlin, vol 7330, pp 167–177 Varadarajan B, Reiley CE, Lin HC, Khudanpur S, Hager GD (2009) Data-derived models for segmentation with application to surgical assessment and training. In: Medical image computing and computer-assisted intervention—MICCAI 2009, Springer, pp 426–434 MalpaniAVedulaSSChenCCGHagerGDA study of crowdsourced segment-level surgical skill assessment using pairwise rankingsInt J Comput Assis Radiol Surg20151091435144710.1007/s11548-015-1238-6 Palm RB (2012) Prediction as a candidate for learning deep hierarchical models of data. Master’s thesis 1386_CR9 1386_CR15 1386_CR8 1386_CR16 1386_CR7 1386_CR6 H Sakoe (1386_CR13) 1978; 26 1386_CR14 1386_CR19 1386_CR17 1386_CR18 1386_CR1 1386_CR5 1386_CR4 1386_CR2 L Zappella (1386_CR21) 2013; 17 Y Bengio (1386_CR3) 2007; 19 A Malpani (1386_CR10) 2015; 10 1386_CR11 1386_CR12 1386_CR20 24505779 - Med Image Comput Comput Assist Interv. 2013;16(Pt 3):339-46 23706754 - Med Image Anal. 2013 Oct;17(7):732-45 26896147 - J Surg Educ. 2016 May-Jun;73(3):482-9 26133652 - Int J Comput Assist Radiol Surg. 2015 Sep;10(9):1435-47 20426016 - Med Image Comput Comput Assist Interv. 2009;12(Pt 1):426-34 24505645 - Med Image Comput Comput Assist Interv. 2013;16(Pt 1):26-33 25320826 - Med Image Comput Comput Assist Interv. 2014;17(Pt 3):409-16 23285532 - Med Image Comput Comput Assist Interv. 2012;15(Pt 1):34-41 |
| References_xml | – reference: Tao L, Zappella L, Hager GD, Vidal R (2013) Surgical gesture segmentation and recognition. In: Medical image computing and computer-assisted intervention—MICCAI 2013, Nagoya, Japan – reference: Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Bejar B, Yuh DD, Chen CCG, Vidal R, Khudanpur S, Hager GD (2014) The JHU-ISI gesture and skill assessment dataset (JIGSAWS): a surgical activity working set for human motion modeling. In: Medical image computing and computer-assisted intervention M2CAI—MICCAI workshop – reference: Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning—ICML ’08, pp 1096–1103 – reference: Lea C, Hager GD, Vidal R (2015) An improved model for segmentation and recognition of fine-grained activities with application to surgical training tasks. In: 2015 IEEE Winter Conference on applications of computer vision (WACV), pp 1123–1129 – reference: Lea C, Vidal R, Hager GD (2016) Learning convolutional action primitives from multimodal timeseries data. In: Proceedings of the IEEE international conference on robotics and automation—ICRA 2016 (Accepted) – reference: Ahmidi N, Gao Y, Béjar B, Vedula SS, Khudanpur S, Vidal R, Hager GD (2013) String motif-based description of tool motion for detecting skill and gestures in robotic surgery. In: Medical image computing and computer-assisted intervention–MICCAI 2013. Springer, Nagoya, Japan – reference: BengioYLamblinPPopoviciDLarochelleHGreedy layer-wise training of deep networksAdv Neural Inf Process Syst200719153160 – reference: Twinanda AP, de Mathelin M, Padoy N (2014) Fisher kernel based task boundary retrieval in laparoscopic database with single video query. In: Medical image computing and computer-assisted intervention—MICCAI 2014, Boston, MA – reference: Varadarajan B, Reiley CE, Lin HC, Khudanpur S, Hager GD (2009) Data-derived models for segmentation with application to surgical assessment and training. In: Medical image computing and computer-assisted intervention—MICCAI 2009, Springer, pp 426–434 – reference: Vedula SS, Malpani A, Ahmidi N, Khudanpur S, Hager G, Chen CCG (2016) Task-level vs. segment-level quantitative metrics for surgical skill assessment. J Surg Educ 73(2). doi:10.1016/j.jsurg.2015.11.009 – reference: MalpaniAVedulaSSChenCCGHagerGDA study of crowdsourced segment-level surgical skill assessment using pairwise rankingsInt J Comput Assis Radiol Surg20151091435144710.1007/s11548-015-1238-6 – reference: Sefati S, Cowan NJ, Vidal R (2015) Learning shared, discriminative dictionaries for surgical gesture segmentation and classification. In: Modeling and monitoring of computer assisted interventions (M2CAI)—MICCAI workshop – reference: Béjar B, Zappella L, Vidal R (2012) Surgical gesture classification from video data. In: Medical image computing and computer-assisted intervention—MICCAI 2012. Springer, Nice, France, pp 34–41 – reference: Muller M (2007) Dynamic time warping. In: Information retrieval for music and motion. Springer, New York – reference: Hazen T, Shen W, White C (2009) Query-by-example spoken term detection using phonetic posteriorgram templates. In: IEEE workshop on automatic speech recognition understanding, 2009. ASRU 2009, pp 421–426 – reference: Carlin M, Thomas S, Jansen A, Hermansky H (2011) Rapid evaluation of speech representations for spoken term discovery. In: Proceedings of the annual conference of the international speech communication association, INTERSPEECH, pp 821–824 – reference: ZappellaLBéjarBHagerGDVidalRSurgical gesture classification from video and kinematic dataMed Image Anal20131773274510.1016/j.media.2013.04.00723706754 – reference: SakoeHChibaSDynamic programming algorithm optimization for spoken word recognitionIEEE Trans Acoust Speech Signal Process1978261434910.1109/TASSP.1978.1163055 – reference: Palm RB (2012) Prediction as a candidate for learning deep hierarchical models of data. Master’s thesis – reference: Gao Y, Vedula SS, Lee GI, Lee MR, Khudanpur S, Hager GD (2016) Unsupervised surgical data alignment with application to automatic activity annotation. In: Proceedings of the IEEE international conference on robotics and automation—ICRA 2016 (Accepted) – reference: Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R (2012) Sparsehidden markov models for surgical gesture classification and skill evaluation. In: Information processing in computer-assisted interventions. Springer, Berlin, vol 7330, pp 167–177 – ident: 1386_CR6 doi: 10.1109/ICRA.2016.7487608 – ident: 1386_CR16 doi: 10.1007/978-3-642-40760-4_43 – ident: 1386_CR4 doi: 10.21437/Interspeech.2011-304 – ident: 1386_CR11 doi: 10.1007/978-3-540-74048-3_4 – volume: 17 start-page: 732 year: 2013 ident: 1386_CR21 publication-title: Med Image Anal doi: 10.1016/j.media.2013.04.007 – ident: 1386_CR5 – ident: 1386_CR1 doi: 10.1007/978-3-642-40811-3_4 – volume: 19 start-page: 153 year: 2007 ident: 1386_CR3 publication-title: Adv Neural Inf Process Syst – ident: 1386_CR19 doi: 10.1016/j.jsurg.2015.11.009 – ident: 1386_CR20 doi: 10.1145/1390156.1390294 – volume: 26 start-page: 43 issue: 1 year: 1978 ident: 1386_CR13 publication-title: IEEE Trans Acoust Speech Signal Process doi: 10.1109/TASSP.1978.1163055 – ident: 1386_CR15 doi: 10.1007/978-3-642-30618-1_17 – ident: 1386_CR9 doi: 10.1109/ICRA.2016.7487305 – volume: 10 start-page: 1435 issue: 9 year: 2015 ident: 1386_CR10 publication-title: Int J Comput Assis Radiol Surg doi: 10.1007/s11548-015-1238-6 – ident: 1386_CR14 – ident: 1386_CR18 doi: 10.1007/978-3-642-04268-3_53 – ident: 1386_CR12 – ident: 1386_CR7 doi: 10.1109/ASRU.2009.5372889 – ident: 1386_CR17 doi: 10.1007/978-3-319-10443-0_52 – ident: 1386_CR2 doi: 10.1007/978-3-642-33415-3_5 – ident: 1386_CR8 doi: 10.1109/WACV.2015.154 – reference: 23706754 - Med Image Anal. 2013 Oct;17(7):732-45 – reference: 20426016 - Med Image Comput Comput Assist Interv. 2009;12(Pt 1):426-34 – reference: 24505645 - Med Image Comput Comput Assist Interv. 2013;16(Pt 1):26-33 – reference: 25320826 - Med Image Comput Comput Assist Interv. 2014;17(Pt 3):409-16 – reference: 26896147 - J Surg Educ. 2016 May-Jun;73(3):482-9 – reference: 24505779 - Med Image Comput Comput Assist Interv. 2013;16(Pt 3):339-46 – reference: 26133652 - Int J Comput Assist Radiol Surg. 2015 Sep;10(9):1435-47 – reference: 23285532 - Med Image Comput Comput Assist Interv. 2012;15(Pt 1):34-41 |
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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... Easy acquisition of surgical data opens many opportunities to automate skill evaluation and teaching. Current technology to search tool motion data for... PURPOSEEasy acquisition of surgical data opens many opportunities to automate skill evaluation and teaching. Current technology to search tool motion data for... |
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| SubjectTerms | 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 |
| Title | Query-by-example surgical activity detection |
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