Abstract
Over the last couple of decades, the quality of surgical interventions has improved owing to the use of computer vision and robotic assistance. One such application of computer vision, namely, detection of surgical tools in videos is gaining attention of the medical image processing community. The main motivation for detection, localization, and annotation of surgical tools is to develop applications for surgical wsorkflow analysis. Such an analysis can aid in report generation, real-time decision support, etc. Cataract surgery is one of the common surgical procedure where surgeons do have direct visual access to the surgical site. Extremely small tools are used for this procedure and the surgeons observe the surgical site through a surgical microscope. In such cases, detecting the presence of tools can act an additional aid to the surgeon as well as other surgical staffs. We propose a framework consisting of a Convolutional Neural Network (CNN) which learns to distinguish and detect the presence of various surgical tools by learning robust features from the frames of a surgical video. Various deep neural architectures are hence evaluated for the task of detecting tools. The baseline models used for the purpose are pretrained on Imagenet dataset and they render upto 50% prediction accuracy. All the experiments have been validated on the dataset released as part of the Cataracts Grand Challenge. A framework for localization and detection of tools has also been proposed, which is capable of extracting visual features from glimpses of an image, by adaptively selecting and processing only the selected regions at high resolution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al Hajj H, Lamard M, Charrière K, Cochener B, Quellec G (2017) Surgical tool detection in cataract surgery videos through multi-image fusion inside a convolutional neural network. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2002–2005
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
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 Imag 36(1):86–97
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Banerjee, N., Sathish, R., Sheet, D. (2019). Deep Neural Architecture for Localization and Tracking of Surgical Tools in Cataract Surgery. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-04061-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04060-4
Online ISBN: 978-3-030-04061-1
eBook Packages: EngineeringEngineering (R0)