Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Esther Kho*, Behdad Dashtbozorg, Lisanne L. de Boer, Koen K. van de Vijver, Henricus J.C.M. Sterenborg, Theo J.M. Ruers

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
15 Downloads (Pure)

Abstract

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650 nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome.

Original languageEnglish
Pages (from-to)4496-4515
Number of pages20
JournalBiomedical optics express
Volume10
Issue number9
DOIs
Publication statusPublished - 1 Sep 2019

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