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Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder

conference contribution
posted on 2017-01-01, 00:00 authored by Mohamed Hassan Attia, Mohammed Hossny, Saeid Nahavandi, Hamed AsadiHamed Asadi
Surgical tool segmentation is used for detection, tracking and pose estimation of the tools in the vicinity of surgical scenes. It is considered as an essential task in surgical phase recognition and flow identification. Surgical flow identification is an unresolved task in the domain of context-aware surgical systems, which is used extensively on computer assisted intervention (CAI). CAI is used for staff assignment, automated guidance during intervention, surgical alert systems, automatic indexing of surgical video databases and optimisation of the real-time scheduling of operating room. Semantic segmentation is used for accurate delineation of surgical tools from the background. In semantic segmentation, each label is assigned to a class as a tool or a background. In this presented work, we applied a hybrid method utilising both recurrent and convolutional networks to achieve higher accuracy of surgical tools segmentation. The proposed method is trained and tested using a public dataset MICCAI 2016 Endoscopic Vision Challenge Robotic Instruments dataset “EndoVis”. We achieved better performance using the proposed method compared to state-of-the-art methods on the same dataset for benchmarking. We achieved a balanced accuracy of 93.3% and Jaccard index of 82.7%.

History

Event

IEEE Systems, Man, and Cybernetics Society. Conference (2017 : Banff, Alta.)

Series

IEEE Systems, Man, and Cybernetics Society Conference

Pagination

3373 - 3378

Publisher

Institute of Electrical and Electronics Engineers

Location

Banff, Alta.

Place of publication

Piscataway, N.J.

Start date

2017-10-05

End date

2017-10-08

ISBN-13

9781538616451

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2017, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

IEEE SMC 2017 : Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics