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Research Article
Manuel Barberio
IRCAD
Corresponding Author
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Nerves are difficult to recognize during surgery and inadvertent injuries may occur, bringing catastrophic consequences for the patient. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing biological tissue property quantification. We show for the first time that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in-vivo at the pixel level. An animal model is used comprising eight anesthetized pigs with a neck midline incision, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models have been trained to recognize these tissue types in HSI data. The best model is a Convolutional Neural Network (CNN), achieving an overall average sensitivity of 0.91 and specificity of 0.99, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieves an average sensitivity of 0.76 and specificity of 1.0. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition.
Keywords
Vivo Hyperspectral, Intraoperative Nerves, Convolutional Neural Network
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Competing interest reported. J.M. is the President of IRCAD, which is partly funded by KARL STORZ, and Medtronic. M.D. is member of the Scientific Board of Diagnostic Green GmbH and is the recipient of the ELIOS grant. M.B., T.C., V.B., R.N., E.F.; M.V. and A.H. have nothing to disclose.
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Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 09 Apr, 2021
No community comments so far
Editorial decision: Major revision
On 02 Jun, 2021
Reviews received
Received 24 Apr, 2021
Reviewers agreed
On 14 Apr, 2021
Reviewers invited
Invitations sent on 14 Apr, 2021
Editor assigned
On 14 Apr, 2021
Editor invited
On 08 Apr, 2021
Submission checks complete
On 07 Apr, 2021
First submitted
On 04 Apr, 2021
Metrics
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Subject Areas
Cancer Biology
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A new preprint design is coming!
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Manuel Barberio
IRCAD
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 09 Apr, 2021
No community comments so far
Editorial decision: Major revision
On 02 Jun, 2021
Reviews received
Received 24 Apr, 2021
Reviewers agreed
On 14 Apr, 2021
Reviewers invited
Invitations sent on 14 Apr, 2021
Editor assigned
On 14 Apr, 2021
Editor invited
On 08 Apr, 2021
Submission checks complete
On 07 Apr, 2021
First submitted
On 04 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Nerves are difficult to recognize during surgery and inadvertent injuries may occur, bringing catastrophic consequences for the patient. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing biological tissue property quantification. We show for the first time that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in-vivo at the pixel level. An animal model is used comprising eight anesthetized pigs with a neck midline incision, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models have been trained to recognize these tissue types in HSI data. The best model is a Convolutional Neural Network (CNN), achieving an overall average sensitivity of 0.91 and specificity of 0.99, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieves an average sensitivity of 0.76 and specificity of 1.0. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Competing interest reported. J.M. is the President of IRCAD, which is partly funded by KARL STORZ, and Medtronic. M.D. is member of the Scientific Board of Diagnostic Green GmbH and is the recipient of the ELIOS grant. M.B., T.C., V.B., R.N., E.F.; M.V. and A.H. have nothing to disclose.
This is a list of supplementary files associated with this preprint. Click to download.
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