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Simultaneous depth estimation and surgical tool segmentation in laparoscopic images
File(s)
Author(s)
Huang, Baoru
Nguyen, Anh
Wang, Siyao
Wang, Ziyang
Mayer, Erik
Type
Journal Article
Abstract
Surgical instrument segmentation and depth estimation are crucial steps to improve autonomy in robotic surgery. Most recent works treat these problems separately, making the deployment challenging. In this paper, we propose a unified framework for depth estimation and surgical tool segmentation in laparoscopic images. The network has an encoder-decoder architecture and comprises two branches for simultaneously performing depth estimation and segmentation. To train the network end to end, we propose a new multi-task loss function that effectively learns to estimate depth in an unsupervised manner, while requiring only semi-ground truth for surgical tool segmentation. We conducted extensive experiments on different datasets to validate these findings. The results showed that the end-to-end network successfully improved the state-of-the-art for both tasks while reducing the complexity during their deployment.
Date Issued
2022-05
Date Acceptance
2022-04-01
Citation
IEEE Transactions on Medical Robotics and Bionics, 2022, 4 (2), pp.335-338
URI
URL
DOI
ISSN
2576-3202
Publisher
Institute of Electrical and Electronics Engineers
Start Page
335
End Page
338
Journal / Book Title
IEEE Transactions on Medical Robotics and Bionics
Volume
4
Issue
2
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
National Institute for Health Research
Cancer Research UK
Imperial College Healthcare NHS Trust- BRC Funding
Identifier
https://ieeexplore.ieee.org/document/9762754
Grant Number
NIHR200035
25147
RDB04
Publication Status
Published
Date Publish Online
2022-04-25