Cerón, JCÁ, Ruiz, GO, Chang, L et al. (1 more author) (2022) Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion. Medical Image Analysis, 81. 102569. p. 102569. ISSN 1361-8415
Abstract
Precise instrument segmentation aids surgeons to navigate the body more easily and increases patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to: (1) a complex surgical environment, and (2) model design trade-off in terms of both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios. The Robust Medical Instrument Segmentation 2019 challenge (ROBUST-MIS) provides more than 10,000 frames with surgical tools in different clinical settings. In this paper, we propose a light-weight single stage instance segmentation model complemented with a convolutional block attention module for achieving both faster and accurate inference. We further improve accuracy through data augmentation and optimal anchor localisation strategies. To our knowledge, this is the first work that explicitly focuses on both real-time performance and improved accuracy. Our approach out-performed top team performances in the most recent edition of ROBUST-MIS challenge with over 44% improvement on area-based multi-instance dice metric MI_DSC and 39% on distance-based multi-instance normalized surface dice MI_NSD. We also demonstrate real-time performance (>60) ( frames-per-second) with different but competitive variants of our final approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Deep learning; MIS instance segmentation; Real-time; Single-stage; Attention; Multi-scale feature fusion |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Aug 2022 10:40 |
Last Modified: | 29 Nov 2022 02:22 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.media.2022.102569 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189777 |