Classification of hyperspectral endocrine tissue images using support vector machines.
computer assisted surgery
head and neck
imaged guided surgery
intraoperative imaging
surgery
thyroidectomy
Journal
The international journal of medical robotics + computer assisted surgery : MRCAS
ISSN: 1478-596X
Titre abrégé: Int J Med Robot
Pays: England
ID NLM: 101250764
Informations de publication
Date de publication:
Oct 2020
Oct 2020
Historique:
received:
03
02
2020
revised:
04
05
2020
accepted:
04
05
2020
pubmed:
12
5
2020
medline:
19
8
2021
entrez:
12
5
2020
Statut:
ppublish
Résumé
Thyroidectomy is one of the most commonly performed surgical procedures. The region of the neck has a very complex structural organization. It would be beneficial to introduce a tool that can assist the surgeon in tissue discrimination during the procedure. One such solution is the noninvasive and contactless technique, called hyperspectral imaging (HSI). To interpret the HSI data, we implemented a supervised classification method to automatically discriminate the parathyroid, the thyroid, and the recurrent laryngeal nerve from surrounding tissue(muscle, skin) and materials (instruments, gauze). A leave-one-patient-out cross-validation was performed. The best performance was obtained using support vector machine (SVM) with a classification and visualization in less than 1.4 seconds. A mean patient accuracy of 68% ± 23% was obtained for all tissues and material types. The proposed method showed promising results and have to be confirmed on a larger cohort of patient data.
Sections du résumé
BACKGROUND
BACKGROUND
Thyroidectomy is one of the most commonly performed surgical procedures. The region of the neck has a very complex structural organization. It would be beneficial to introduce a tool that can assist the surgeon in tissue discrimination during the procedure. One such solution is the noninvasive and contactless technique, called hyperspectral imaging (HSI).
METHODS
METHODS
To interpret the HSI data, we implemented a supervised classification method to automatically discriminate the parathyroid, the thyroid, and the recurrent laryngeal nerve from surrounding tissue(muscle, skin) and materials (instruments, gauze). A leave-one-patient-out cross-validation was performed.
RESULTS
RESULTS
The best performance was obtained using support vector machine (SVM) with a classification and visualization in less than 1.4 seconds. A mean patient accuracy of 68% ± 23% was obtained for all tissues and material types.
CONCLUSIONS
CONCLUSIONS
The proposed method showed promising results and have to be confirmed on a larger cohort of patient data.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-10Subventions
Organisme : Federal Ministry of Education and Research
ID : 13GW0248B
Informations de copyright
© 2020 The Authors. The International Journal of Medical Robotics and Computer Assisted Surgery published by John Wiley & Sons Ltd.
Références
Adam MA, Thomas S, Youngwirth L, et al. Is there a minimum number of thyroidectomies a surgeon should perform to optimize patient outcomes? Ann Surg. 2017;265(2):402-407. https://doi.org/10.1097/SLA.0000000000001688.
Falco J, Dip F, Quadri P, de la Fuente M, Prunello M, Rosenthal RJ. Increased identification of parathyroid glands using near infrared light during thyroid and parathyroid surgery. Surg Endosc. 2017;31(9):3737-3742. https://doi.org/10.1007/s00464-017-5424-1.
McWade MA, Paras C, White LM, et al. Label-free intraoperative parathyroid localization with near-infrared autofluorescence imaging. J Clin Endocrinol Metabol. 2014;99(12):4574-4580. https://doi.org/10.1210/jc.2014-2503.
De Leeuw F, Breuskin I, Abbaci M, et al. Intraoperative near-infrared imaging for parathyroid gland identification by auto-fluorescence: a feasibility study. World J Surg. 2016;40(9):2131-2138. https://doi.org/10.1007/s00268-016-3571-5.
Kim SW, Lee HS, Lee KD. Intraoperative real-time localization of parathyroid gland with near infrared fluorescence imaging. Gland Surg. 2017;6(5):516-524. https://doi.org/10.21037/gs.2017.05.08.
Serpell JW, Lee JC, Yeung MJ, Grodski S, Johnson W, Bailey M. Differential recurrent laryngeal nerve palsy rates after thyroidectomy. Surgery. 2014;156(5):1157-1166. https://doi.org/10.1016/j.surg.2014.07.018.
Barberio M, Maktabi M, Gockel I, et al. Hyperspectral based discrimination of thyroid and parathyroid during surgery. Curr Direct Biomed Eng. 2018;4(1):399-402. https://doi.org/10.1515/cdbme-2018-0095.
Ahmed R, Khan N, Ellemdin S, Gayaparsad K. Ultrasound-first imaging modality in the detection of parathyroid adenomas. South African J Radiol. 2008;12(3):64. https://doi.org/10.4102/sajr.v12i3.558.
Tummers QRJG, Schepers A, Hamming JF, et al. Intraoperative guidance in parathyroid surgery using near-infrared fluorescence imaging and low-dose Methylene Blue. Surgery. 2015;158(5):1323-1330. https://doi.org/10.1016/j.surg.2015.03.027.
Yu HW, Chung JW, Yi JW, et al. Intraoperative localization of the parathyroid glands with indocyanine green and Firefly(R) technology during BABA robotic thyroidectomy. Surg Endosc. 2017;31(7):3020-3027. https://doi.org/10.1007/s00464-016-5330-y.
Zaidi N, Bucak E, Okoh A, Yazici P, Yigitbas H, Berber E. The utility of indocyanine green near infrared fluorescent imaging in the identification of parathyroid glands during surgery for primary hyperparathyroidism: ICG Fluorescent Imaging During Parathyroidectomy. J Surg Oncol. 2016;113(7):771-774. https://doi.org/10.1002/jso.24240.
Falco J, Dip F, Quadri P, de la Fuente M, Rosenthal R. Cutting edge in thyroid surgery: autofluorescence of parathyroid glands. J Am Coll Surg. 2016;223(2):374-380. https://doi.org/10.1016/j.jamcollsurg.2016.04.049.
Ladurner R, Al Arabi N, Guendogar U, Hallfeldt K, Stepp H, Gallwas J. Near-infrared autofluorescence imaging to detect parathyroid glands in thyroid surgery. Ann R Coll Surg Engl. 2018;100(1):33-36. https://doi.org/10.1308/rcsann.2017.0102.
Benmiloud F, Rebaudet S, Varoquaux A, Penaranda G, Bannier M, Denizot A. Impact of autofluorescence-based identification of parathyroids during total thyroidectomy on postoperative hypocalcemia: a before and after controlled study. Surgery. 2018;163(1):23-30. https://doi.org/10.1016/j.surg.2017.06.022.
Kahramangil B, Dip F, Benmiloud F, et al. Detection of parathyroid autofluorescence using near-infrared imaging: a multicenter analysis of concordance between different surgeons. Ann Surg Oncol. 2018;25(4):957-962. https://doi.org/10.1245/s10434-018-6364-2.
Koch M, Ntziachristos V. Advancing surgical vision with fluorescence imaging. Annu Rev Med. 2016;67(1):153-164. https://doi.org/10.1146/annurev-med-051914-022043.
Pollack G, Pollack A, Delfiner J, Fernandez J. Parathyroid surgery and methylene blue: a review with guidelines for safe intraoperative use. Laryngoscope. 2009;119(10):1941-1946. https://doi.org/10.1002/lary.20581.
McWade MA, Sanders ME, Broome JT, Solórzano CC, Mahadevan-Jansen A. Establishing the clinical utility of autofluorescence spectroscopy for parathyroid detection. Surgery. 2016;159(1):193-203. https://doi.org/10.1016/j.surg.2015.06.047.
Halicek M, Fabelo H, Ortega S, Callico GM, Fei B. In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer. Cancer. 2019;11(6):756. https://doi.org/10.3390/cancers11060756.
Fabelo H, Ortega S, Ravi D, et al. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLOS ONE. 2018;13(3):e0193721. https://doi.org/10.1371/journal.pone.0193721.
Fei B, Lu G, Wang X, et al. Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients. J Biomed Opt. 2017;22(08):1. https://doi.org/10.1117/1.JBO.22.8.086009.
Liu Z, Wang H, Li Q. Tongue tumor detection in medical hyperspectral images. Sensors. 2011;12(1):162-174. https://doi.org/10.3390/s120100162.
Schols RM, Bouvy ND, Amelink A, Wieringa FP, Alic L. Differentiation between nerve and adipose tissue using wideband (350-1,830 nm) in vivo diffuse reflectance spectroscopy. Lasers Surg Med. 2014;46(7):538-545.
Hosking A-M, Coakley BJ, Chang D, et al. Hyperspectral imaging in automated digital dermoscopy screening for melanoma: hyperspectral dermoscopy. Lasers Surg Med. 2019;17:214-222. https://doi.org/10.1002/lsm.23055.
Ortega S, Fabelo H, Camacho R, de la Luz PM, Callicó GM, Sarmiento R. Detecting brain tumor in pathological slides using hyperspectral imaging. Biomed Opt Express. 2018;9(2):818-831. https://doi.org/10.1364/BOE.9.000818.
Akbari H, Halig LV, Schuster DM, et al. Hyperspectral imaging and quantitative analysis for prostate cancer detection. J Biomed Opt. 2012;17(7):0760051. https://doi.org/10.1117/1.JBO.17.7.076005.
Nouri D, Lucas Y, Treuillet S. Hyperspectral interventional imaging for enhanced tissue visualization and discrimination combining band selection methods. Int J Comput Assist Radiol Surg. 2016;11(12):2185-2197. https://doi.org/10.1007/s11548-016-1449-5.
Ghamisi P, Plaza J, Chen Y, Li J, Plaza AJ. Advanced spectral classifiers for hyperspectral images: a review. IEEE Geosci Remote Sensing Mag. 2017;5(1):8-32. https://doi.org/10.1109/MGRS.2016.2616418.
Kulcke A, Holmer A, Wahl P, Siemers F, Wild T, Daeschlein G. A compact hyperspectral camera for measurement of perfusion parameters in medicine. Biomed Tech (Berl). 2018;63(5):519-527.
Moulla Y, Reifenrath M, Rehmet K, et al. Hybridösophagektomie mit intraoperativem Hyperspektral-Imaging: Videobeitrag. Chirurg. 2020;17. https://doi.org/10.1007/s00104-020-01139-1.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321-357. https://doi.org/10.1613/jair.953.
Boughorbel S, Jarray F, El-Anbari M. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLOS ONE. 2017;12(6):e0177678. https://doi.org/10.1371/journal.pone.0177678.
Wisotzky EL, Uecker FC, Arens P, Dommerich S, Hilsmann A, Eisert P. Intraoperative hyperspectral determination of human tissue properties. J Biomed Opt. 2018;23(09):1. https://doi.org/10.1117/1.JBO.23.9.091409.
Thomas G, McWade MA, Nguyen JQ, et al. Innovative surgical guidance for label-free real-time parathyroid identification. Surgery. 2019;165(1):114-123. https://doi.org/10.1016/j.surg.2018.04.079.
Schols RM, Alic L, Wieringa FP, Bouvy ND, Stassen LPS. Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery: Automated spectroscopic tissue classification. Int J Med Robot. 2017;13(1):e1748. https:doi.org/10.1002/rcs.1748.
Squires MH, Shirley LA, Shen C, Jarvis R, Phay JE. Intraoperative autofluorescence parathyroid identification in patients with multiple endocrine Neoplasia type 1. JAMA Otolaryngol Head Neck Surg. 2019;145(10):897. https://doi.org/10.1001/jamaoto.2019.1987.