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用于体内小肠坏死组织检测的可见近红外高光谱成像与监督分类

Visible near-infrared hyperspectral imaging and supervised classification for the detection of small intestinal necrosis tissue in vivo.

作者信息

Zhang LeChao, Huang DanFei, Chen XiaoJing, Zhu LiBin, Chen XiaoQing, Xie ZhongHao, Huang GuangZao, Gao JunZhao, Shi Wen, Cui GuiHua

机构信息

College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, 130000, China.

Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, Guangdong, 528400, China.

出版信息

Biomed Opt Express. 2022 Oct 27;13(11):6061-6080. doi: 10.1364/BOE.470202. eCollection 2022 Nov 1.

Abstract

Complete recognition of necrotic areas during small bowel tissue resection remains challenging due to the lack of optimal intraoperative aid identification techniques. This research utilizes hyperspectral imaging techniques to automatically distinguish normal and necrotic areas of small intestinal tissue. Sample data were obtained from the animal model of small intestinal tissue of eight Japanese large-eared white rabbits developed by experienced physicians. A spectral library of normal and necrotic regions of small intestinal tissue was created and processed using six different supervised classification algorithms. The results show that hyperspectral imaging combined with supervised classification algorithms can be a suitable technique to automatically distinguish between normal and necrotic areas of small intestinal tissue. This new technique could aid physicians in objectively identify normal and necrotic areas of small intestinal tissue.

摘要

由于缺乏最佳的术中辅助识别技术,在小肠组织切除术中完整识别坏死区域仍然具有挑战性。本研究利用高光谱成像技术自动区分小肠组织的正常区域和坏死区域。样本数据取自由经验丰富的医生建立的八只日本大耳白兔小肠组织动物模型。创建了小肠组织正常区域和坏死区域的光谱库,并使用六种不同的监督分类算法进行处理。结果表明,高光谱成像结合监督分类算法可以成为自动区分小肠组织正常区域和坏死区域的合适技术。这项新技术可以帮助医生客观地识别小肠组织的正常区域和坏死区域。

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