高光谱成像
边距(机器学习)
乳腺癌
乳腺肿瘤
癌症检测
光谱成像
癌症
生物医学工程
医学
地质学
遥感
计算机科学
机器学习
内科学
作者
Esther Kho,Lisanne L. de Boer,Anouk L. Post,Koen Van de Vijver,Katarzyna Jóźwiak,Henricus J. C. M. Sterenborg,Theo J. M. Ruers
标识
DOI:10.1002/jbio.201900086
摘要
Hyperspectral imaging is a promising technique for resection margin assessment during cancer surgery. Thereby, only a specific amount of the tissue below the resection surface, the clinically defined margin width, should be assessed. Since the imaging depth of hyperspectral imaging varies with wavelength and tissue composition, this can have consequences for the clinical use of hyperspectral imaging as margin assessment technique. In this study, a method was developed that allows for hyperspectral analysis of resection margins in breast cancer. This method uses the spectral slope of the diffuse reflectance spectrum at wavelength regions where the imaging depth in tumor and healthy tissue is equal. Thereby, tumor can be discriminated from healthy breast tissue while imaging up to a similar depth as the required tumor-free margin width of 2 mm. Applying this method to hyperspectral images acquired during surgery would allow for robust margin assessment of resected specimens. In this paper, we focused on breast cancer, but the same approach can be applied to develop a method for other types of cancer.
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