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Classification of hyperspectral endocrine tissue images using support vector machines
Author(s) -
Maktabi Marianne,
Köhler Hannes,
Ivanova Magarita,
Neumuth Thomas,
Rayes Nada,
Seidemann Lena,
Sucher Robert,
JansenWinkeln Boris,
Gockel Ines,
Barberio Manuel,
Chalopin Claire
Publication year - 2020
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.2121
Subject(s) - support vector machine , computer science , artificial intelligence , visualization , pattern recognition (psychology) , hyperspectral imaging , computer vision , medicine
Abstract 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 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 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 The proposed method showed promising results and have to be confirmed on a larger cohort of patient data.