Hyperspectral based discrimination of thyroid and parathyroid during surgery

Unintended injuring of anatomical structures during endocrine neck operations can have severe consequences for patient. Especially the nerves and the parathyroid gland can be hard to identify visually. Therefore, intraoperative methods are needed to support the surgeon in this task. Hyperspectral im...

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Main Authors: Barberio Manuel, Maktabi Marianne, Gockel Ines, Rayes Nada, Jansen-Winkeln Boris, Köhler Hannes, Rabe Sebastian M., Seidemann Lena, Takoh Jonathan P., Diana Michele, Neumuth Thomas, Chalopin Claire
Format: Article
Language:English
Published: De Gruyter 2018-09-01
Series:Current Directions in Biomedical Engineering
Subjects:
Online Access:https://doi.org/10.1515/cdbme-2018-0095
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author Barberio Manuel
Maktabi Marianne
Gockel Ines
Rayes Nada
Jansen-Winkeln Boris
Köhler Hannes
Rabe Sebastian M.
Seidemann Lena
Takoh Jonathan P.
Diana Michele
Neumuth Thomas
Chalopin Claire
author_facet Barberio Manuel
Maktabi Marianne
Gockel Ines
Rayes Nada
Jansen-Winkeln Boris
Köhler Hannes
Rabe Sebastian M.
Seidemann Lena
Takoh Jonathan P.
Diana Michele
Neumuth Thomas
Chalopin Claire
author_sort Barberio Manuel
collection DOAJ
description Unintended injuring of anatomical structures during endocrine neck operations can have severe consequences for patient. Especially the nerves and the parathyroid gland can be hard to identify visually. Therefore, intraoperative methods are needed to support the surgeon in this task. Hyperspectral imaging (HSI) is a new approach in the medical area which combines a camera with a spectrometer. It showed promising results for the discrimination of tissue. In this work, HSI-data of seven patients were acquired during thyroid and parathyroid operations. The mean absorbance spectra of both glands showed differences in the range between 600 and 700 nm and at 760 and 960 nm. This means that thyroid and parathyroid have different oxygenation states and different contents of deoxygenated hemoglobin and water. From these observations, it is possible to define spectral signatures to characterize both glands. We showed on one patient how spectral signatures can be used in classification algorithms to automatically identify the thyroid and parathyroid from other structures.
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spelling doaj.art-944f14fea0f5475ca969cc25009433192023-08-21T06:42:02ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042018-09-014139940210.1515/cdbme-2018-0095cdbme-2018-0095Hyperspectral based discrimination of thyroid and parathyroid during surgeryBarberio Manuel0Maktabi Marianne1Gockel Ines2Rayes Nada3Jansen-Winkeln Boris4Köhler Hannes5Rabe Sebastian M.6Seidemann Lena7Takoh Jonathan P.8Diana Michele9Neumuth Thomas10Chalopin Claire11Institute of Image-Guided Surgery (IHU),Strasbourg, FranceInnovation Center Computer Assisted Surgery (ICCAS), University of Leipzig,Leipzig, GermanyDepartment of visceral, thorax, transplantation and vascular surgery, University Hospital of Leipzig,Leipzig, GermanyDepartment of visceral, thorax, transplantation and vascular surgery, University Hospital of Leipzig,Leipzig, GermanyDepartment of visceral, thorax, transplantation and vascular surgery, University Hospital of Leipzig,Leipzig, GermanyInnovation Center Computer Assisted Surgery (ICCAS), University of Leipzig,Leipzig, GermanyDepartment of visceral, thorax, transplantation and vascular surgery, University Hospital of Leipzig,Leipzig, GermanyDepartment of visceral, thorax, transplantation and vascular surgery, University Hospital of Leipzig,Leipzig, GermanyDepartment of visceral, thorax, transplantation and vascular surgery, University Hospital of Leipzig,Leipzig, GermanyInstitute of Image-Guided Surgery (IHU),Strasbourg, FranceInnovation Center Computer Assisted Surgery (ICCAS), University of Leipzig,Leipzig, GermanyInnovation Center Computer Assisted Surgery (ICCAS), University of Leipzig,Leipzig, GermanyUnintended injuring of anatomical structures during endocrine neck operations can have severe consequences for patient. Especially the nerves and the parathyroid gland can be hard to identify visually. Therefore, intraoperative methods are needed to support the surgeon in this task. Hyperspectral imaging (HSI) is a new approach in the medical area which combines a camera with a spectrometer. It showed promising results for the discrimination of tissue. In this work, HSI-data of seven patients were acquired during thyroid and parathyroid operations. The mean absorbance spectra of both glands showed differences in the range between 600 and 700 nm and at 760 and 960 nm. This means that thyroid and parathyroid have different oxygenation states and different contents of deoxygenated hemoglobin and water. From these observations, it is possible to define spectral signatures to characterize both glands. We showed on one patient how spectral signatures can be used in classification algorithms to automatically identify the thyroid and parathyroid from other structures.https://doi.org/10.1515/cdbme-2018-0095intraoperative imaginggastrointestinal operationpatient dataspectral signatureclassification algorithms
spellingShingle Barberio Manuel
Maktabi Marianne
Gockel Ines
Rayes Nada
Jansen-Winkeln Boris
Köhler Hannes
Rabe Sebastian M.
Seidemann Lena
Takoh Jonathan P.
Diana Michele
Neumuth Thomas
Chalopin Claire
Hyperspectral based discrimination of thyroid and parathyroid during surgery
Current Directions in Biomedical Engineering
intraoperative imaging
gastrointestinal operation
patient data
spectral signature
classification algorithms
title Hyperspectral based discrimination of thyroid and parathyroid during surgery
title_full Hyperspectral based discrimination of thyroid and parathyroid during surgery
title_fullStr Hyperspectral based discrimination of thyroid and parathyroid during surgery
title_full_unstemmed Hyperspectral based discrimination of thyroid and parathyroid during surgery
title_short Hyperspectral based discrimination of thyroid and parathyroid during surgery
title_sort hyperspectral based discrimination of thyroid and parathyroid during surgery
topic intraoperative imaging
gastrointestinal operation
patient data
spectral signature
classification algorithms
url https://doi.org/10.1515/cdbme-2018-0095
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