MSDE-Net: A Multi-Scale Dual-Encoding Network for Surgical Instrument Segmentation

Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instruments remains challenging due to the complexity of the surgical environment. To tac...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2024-07, Vol.28 (7), p.4072-4083
Main Authors: Yang, Lei, Gu, Yuge, Bian, Guibin, Liu, Yanhong
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Biomedical imaging
Coding
Complementarity
Decoding
dual-branch encoder
Effectiveness
Feature extraction
feature fusion
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Information processing
Instruments
Medical instruments
Microscopes
Minimally Invasive Surgical Procedures - instrumentation
Minimally Invasive Surgical Procedures - methods
Multilayers
Neural networks
Neural Networks, Computer
Receptive field
Robotic surgery
Surgery
Surgical apparatus & instruments
Surgical instrument segmentation
Surgical Instruments
transformer
Transformers
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instruments remains challenging due to the complexity of the surgical environment. To tackle this issue, this paper introduces a novel multiscale dual-encoding segmentation network, termed MSDE-Net, designed to automatically and precisely segment surgical instruments. The proposed MSDE-Net leverages a dual-branch encoder comprising a convolutional neural network (CNN) branch and a transformer branch to effectively extract both local and global features. Moreover, an attention fusion block (AFB) is introduced to ensure effective information complementarity between the dual-branch encoding paths. Additionally, a multilayer context fusion block (MCF) is proposed to enhance the network's capacity to simultaneously extract global and local features. Finally, to extend the scope of global feature information under larger receptive fields, a multi-receptive field fusion (MRF) block is incorporated. Through comprehensive experimental evaluations on two publicly available datasets for surgical instrument segmentation, the proposed MSDE-Net demonstrates superior performance compared to existing methods.
ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2023.3344716