Hyperspectral imaging for tissue classification, a way toward smart laparoscopic colorectal surgery

Elisabeth J.M. Baltussen* (Corresponding Author), Esther N.D. Kok, Susan G. Brouwer de Koning, Joyce Sanders, Arend G.J. Aalbers, Niels F.M. Kok, Geerard L. Beets, Claudie C. Flohil, Sjoerd C. Bruin, Koert F.D. Kuhlmann, Henricus J.C.M. Sterenborg, Theo J.M. Ruers

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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In the last decades, laparoscopic surgery has become the gold standard in patients with colorectal cancer. To overcome the drawback of reduced tactile feedback, real-time tissue classification could be of great benefit. In this ex vivo study, hyperspectral imaging (HSI) was used to distinguish tumor tissue from healthy surrounding tissue. A sample of fat, healthy colorectal wall, and tumor tissue was collected per patient and imaged using two hyperspectral cameras, covering the wavelength range from 400 to 1700 nm. The data were randomly divided into a training (75%) and test (25%) set. After feature reduction, a quadratic classifier and support vector machine were used to distinguish the three tissue types. Tissue samples of 32 patients were imaged using both hyperspectral cameras. The accuracy to distinguish the three tissue types using both hyperspectral cameras was 0.88 (STD  =  0.13) on the test dataset. When the accuracy was determined per patient, a mean accuracy of 0.93 (STD  =  0.12) was obtained on the test dataset. This study shows the potential of using HSI in colorectal cancer surgery for fast tissue classification, which could improve clinical outcome. Future research should be focused on imaging entire colon/rectum specimen and the translation of the technique to an intraoperative setting.

Original languageEnglish
Article number016002
Pages (from-to)1-9
Number of pages9
JournalJournal of biomedical optics
Issue number1
Publication statusPublished - 30 Jan 2019


  • colorectal cancer
  • hyperspectral imaging
  • machine learning
  • margin assessment
  • support vector machine


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