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Weakly supervised segmentation for real-time surgical tool tracking
Surgical tool tracking has a variety of applications in different surgical scenarios. Electromagnetic (EM) tracking can be utilised for tool tracking, but the accuracy is often limited by magnetic interference. Vision-based methods have also been suggested; however, tracking robustness is limited by...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Institution of Engineering and Technology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952260/ https://www.ncbi.nlm.nih.gov/pubmed/32038863 http://dx.doi.org/10.1049/htl.2019.0083 |
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author | Lee, Eung-Joo Plishker, William Liu, Xinyang Bhattacharyya, Shuvra S. Shekhar, Raj |
author_facet | Lee, Eung-Joo Plishker, William Liu, Xinyang Bhattacharyya, Shuvra S. Shekhar, Raj |
author_sort | Lee, Eung-Joo |
collection | PubMed |
description | Surgical tool tracking has a variety of applications in different surgical scenarios. Electromagnetic (EM) tracking can be utilised for tool tracking, but the accuracy is often limited by magnetic interference. Vision-based methods have also been suggested; however, tracking robustness is limited by specular reflection, occlusions, and blurriness observed in the endoscopic image. Recently, deep learning-based methods have shown competitive performance on segmentation and tracking of surgical tools. The main bottleneck of these methods lies in acquiring a sufficient amount of pixel-wise, annotated training data, which demands substantial labour costs. To tackle this issue, the authors propose a weakly supervised method for surgical tool segmentation and tracking based on hybrid sensor systems. They first generate semantic labellings using EM tracking and laparoscopic image processing concurrently. They then train a light-weight deep segmentation network to obtain a binary segmentation mask that enables tool tracking. To the authors’ knowledge, the proposed method is the first to integrate EM tracking and laparoscopic image processing for generation of training labels. They demonstrate that their framework achieves accurate, automatic tool segmentation (i.e. without any manual labelling of the surgical tool to be tracked) and robust tool tracking in laparoscopic image sequences. |
format | Online Article Text |
id | pubmed-6952260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Institution of Engineering and Technology |
record_format | MEDLINE/PubMed |
spelling | pubmed-69522602020-02-07 Weakly supervised segmentation for real-time surgical tool tracking Lee, Eung-Joo Plishker, William Liu, Xinyang Bhattacharyya, Shuvra S. Shekhar, Raj Healthc Technol Lett Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions Surgical tool tracking has a variety of applications in different surgical scenarios. Electromagnetic (EM) tracking can be utilised for tool tracking, but the accuracy is often limited by magnetic interference. Vision-based methods have also been suggested; however, tracking robustness is limited by specular reflection, occlusions, and blurriness observed in the endoscopic image. Recently, deep learning-based methods have shown competitive performance on segmentation and tracking of surgical tools. The main bottleneck of these methods lies in acquiring a sufficient amount of pixel-wise, annotated training data, which demands substantial labour costs. To tackle this issue, the authors propose a weakly supervised method for surgical tool segmentation and tracking based on hybrid sensor systems. They first generate semantic labellings using EM tracking and laparoscopic image processing concurrently. They then train a light-weight deep segmentation network to obtain a binary segmentation mask that enables tool tracking. To the authors’ knowledge, the proposed method is the first to integrate EM tracking and laparoscopic image processing for generation of training labels. They demonstrate that their framework achieves accurate, automatic tool segmentation (i.e. without any manual labelling of the surgical tool to be tracked) and robust tool tracking in laparoscopic image sequences. The Institution of Engineering and Technology 2019-11-26 /pmc/articles/PMC6952260/ /pubmed/32038863 http://dx.doi.org/10.1049/htl.2019.0083 Text en http://creativecommons.org/licenses/by-nc/3.0/ This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/) |
spellingShingle | Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions Lee, Eung-Joo Plishker, William Liu, Xinyang Bhattacharyya, Shuvra S. Shekhar, Raj Weakly supervised segmentation for real-time surgical tool tracking |
title | Weakly supervised segmentation for real-time surgical tool tracking |
title_full | Weakly supervised segmentation for real-time surgical tool tracking |
title_fullStr | Weakly supervised segmentation for real-time surgical tool tracking |
title_full_unstemmed | Weakly supervised segmentation for real-time surgical tool tracking |
title_short | Weakly supervised segmentation for real-time surgical tool tracking |
title_sort | weakly supervised segmentation for real-time surgical tool tracking |
topic | Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952260/ https://www.ncbi.nlm.nih.gov/pubmed/32038863 http://dx.doi.org/10.1049/htl.2019.0083 |
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