Hyperspectral Imaging Combined with Deep Learning to Detect Ischemic Necrosis in Small Intestinal Tissue

• Published in: Photonics Vol. 10; no. 7; p. 708
• Main Authors: Zhang, Lechao, Zhou, Yao, Huang, Danfei, Zhu, Libin, Chen, Xiaoqing, Xie, Zhonghao, Cui, Guihua, Huang, Guangzao, Ali, Shujat, Chen, Xiaojing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2023
• Subjects: Abdomen; Artificial intelligence; Artificial neural networks; automatic identification; Biopsy; Cameras; Classification; Deep learning; Geospatial data; Hyperspectral imaging; Imaging techniques; Intestine; Ischemia; Light; Machine learning; Medical imaging; Necrosis; Neural networks; optical biopsy; Patients; Physicians; Rabbits; Small intestine; small intestine tissue; Thrombosis; Tissues; United States > US
• ISSN: 2304-6732; 2304-6732
• DOI: 10.3390/photonics10070708

Obtaining adequate resection margins in small intestinal necrotic tissue remains challenging due to the lack of intraoperative feedback. Here, we used hyperspectral imaging (HSI), an imaging technique for objective identification, combined with deep learning methods for automated small intestine tissue classification. As part of a prospective experimental study, we recorded hyperspectral datasets of small intestine biopsies from seven white rabbits. Based on the differences in the spectral characteristics of normal and ischemic necrotic small intestinal tissues in the wavelength range of 400–1000 nm, we applied deep learning techniques to objectively distinguish between these two types of tissues. The results showed that three-dimensional convolutional neural networks were more effective in extracting both spectral and spatial features of small intestine tissue hyperspectral data for classification. The combination of a deep learning model and HSI provided a new idea for the objective identification of ischemic necrotic tissue in the small intestine.