Abstract
Purpose
Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgical activities. Current methods for surgical tool tracking in videos need to be trained on data in which the spatial positions of the tools are manually annotated. Generating such training data is difficult and time-consuming. Instead, we propose to use solely binary presence annotations to train a tool tracker for laparoscopic videos.
Methods
The proposed approach is composed of a CNN + Convolutional LSTM (ConvLSTM) neural network trained end to end, but weakly supervised on tool binary presence labels only. We use the ConvLSTM to model the temporal dependencies in the motion of the surgical tools and leverage its spatiotemporal ability to smooth the class peak activations in the localization heat maps (Lh-maps).
Results
We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over \(5.0\%\), \(13.9\%\), and \(12.6\%\), respectively.
Conclusions
In this paper, we demonstrate that binary presence labels are sufficient for training a deep learning tracking model using our proposed method. We also show that the ConvLSTM can leverage the spatiotemporal coherence of consecutive image frames across a surgical video to improve tool presence detection, spatial localization, and motion tracking.
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Notes
This can only arise for the grasper in this dataset.
References
Twinanda AP, Shehata S, Mutter D, Marescaux J, De Mathelin M, Padoy N (2017) Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans Med Imaging 36(1):86–97
Zisimopoulos O, Flouty E, Luengo I, Giataganas P, Nehme J, Chow A, Stoyanov D (2018)Deepphase: surgical phase recognition in cataracts videos. In: MICCAI, pp 265–272
Richa R, Balicki M, Meisner E, Sznitman R, Taylor R, Hager G (2011) Visual tracking of surgical tools for proximity detection in retinal surgery. In: IPCAI, pp 55–66
Sznitman R, Becker C, Fua P (2014) Fast part-based classification for instrument detection in minimally invasive surgery. In: MICCAI, pp 692–699
Vardazaryan A, Mutter D, Marescaux J, Padoy N (2018) Weakly-supervised learning for tool localization in laparoscopic videos. arXiv preprint. arXiv:1806.05573
Sznitman R, Basu A, Richa R, Handa J, Gehlbach P, Taylor RH, Jedynak B, Hager GD (2011) Unified detection and tracking in retinal microsurgery. In: MICCAI, pp 1–8
Al Hajj H, Lamard M, Conze PH, Cochener B, Quellec G (2018) Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks. Med Image Anal 47:203–218
Jin A, Yeung S, Jopling J, Krause J, Azagury D, Milstein A, Fei-Fei L (2018) Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. In: WACV, pp 691–699
Singh KK, Lee YJ (2017) Hide-and-seek: forcing a network to be meticulous for weakly-supervised object and action localization. In: ICCV
Milan A, Rezatofighi SH, Dick AR, Reid ID, Schindler K (2017) Online multi-target tracking using recurrent neural networks. In: AAAI, vol 2, p 4
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770–778
Kuhn HW (1955) The Hungarian method for the assignment problem. Naval Res Logist Q 2(1–2):83–97
Mishra K, Sathish R, Sheet D (2017) Learning latent temporal connectionism of deep residual visual abstractions for identifying surgical tools in laparoscopy procedures. In: CVPR workshops, pp 58–65
Hwang S, Kim HE (2016) Self-transfer learning for weakly supervised lesion localization. In: MICCAI,, pp 239–246
Jia Z, Huang X, Eric I, Chang C, Xu Y (2017) Constrained deep weak supervision for histopathology image segmentation. IEEE Trans Med Imaging 36(11):2376–2388
Zhou Y, Zhu Y, Ye Q, Qiu Q, Jiao J (2018) Weakly supervised instance segmentation using class peak response. arXiv preprint. arXiv:1804.00880
Rieke N, Tan DJ, di San Filippo CA, Tombari F, Alsheakhali M, Belagiannis V, Eslami A, Navab N (2016) Real-time localization of articulated surgical instruments in retinal microsurgery. Med Image Anal 34:82–100
Sznitman R, Ali K, Richa R, Taylor RH, Hager GD, Fua P (2012) Data-driven visual tracking in retinal microsurgery. In: MICCAI, pp 568–575
Bouget D, Allan M, Stoyanov D, Jannin P (2017) Vision-based and marker-less surgical tool detection and tracking: a review of the literature. Med Image Anal 35:633–654
Lo BP, Darzi A, Yang GZ (2003) Episode classification for the analysis of tissue/instrument interaction with multiple visual cues. In: MICCAI, pp 230–237
Luo W, Yang B, Urtasun R (2018) Fast and furious: real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In: CVPR, pp 3569–3577
Liu M, Zhu M (2017) Mobile video object detection with temporally-aware feature maps. arXiv preprint. arXiv:1711.06368
Durand T, Mordan T, Thome N, Cord M (2017) Wildcat: weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation. In: CVPR, vol 2
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Wei H, Zhou H, Sankaranarayanan J, Sengupta S, Samet H (2018) Residual convolutional lstm for tweet count prediction. In: Companion of the the web conference 2018 on the web conference 2018, pp 1309–1316. International World Wide Web Conferences Steering Committee
Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: the clear mot metrics. J Image Video Process 2008:1
Acknowledgements
This work was supported by French state funds managed within the Investissements dAvenir program by BPI France (Project CONDOR) and by the ANR (References ANR-11-LABX-0004 and ANR-10-IAHU-02).
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Nwoye, C.I., Mutter, D., Marescaux, J. et al. Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. Int J CARS 14, 1059–1067 (2019). https://doi.org/10.1007/s11548-019-01958-6
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DOI: https://doi.org/10.1007/s11548-019-01958-6