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首页> 外文会议>Optical Coherence tomography and coherence domain optical methods in biomedicine XX >Ex vivo brain tumor analysis using Spectroscopic Optical Coherence Tomography
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Ex vivo brain tumor analysis using Spectroscopic Optical Coherence Tomography

机译：使用光谱光学相干断层扫描进行离体脑肿瘤分析

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摘要

A big challenge during neurosurgeries is to distinguish between healthy tissue and cancerous tissue, but currently a suitable non-invasive real time imaging modality is not available. Optical Coherence Tomography (OCT) is a potential technique for such a modality. OCT has a penetration depth of 1-2 mm and a resolution of 1-15 μm which is sufficient to illustrate structural differences between healthy tissue and brain tumor. Therefore, we investigated gray and white matter of healthy central nervous system and meningioma samples with a Spectral Domain OCT System (Thorlabs Callisto). Additional OCT images were generated after paraffin embedding and after the samples were cut into 10 μm thin slices for histological investigation with a bright field microscope. All samples were stained with Hematoxylin and Eosin. In all cases B-scans and 3D images were made. Furthermore, a camera image of the investigated area was made by the built-in video camera of our OCT system. For orientation, the backsides of all samples were marked with blue ink. The structural differences between healthy tissue and meningioma samples were most pronounced directly after removal. After paraffin embedding these differences diminished. A correlation between OCT en face images and microscopy images can be seen. In order to increase contrast, post processing algorithms were applied. Hence we employed Spectroscopic OCT, pattern recognition algorithms and machine learning algorithms such as k-means Clustering and Principal Component Analysis.
机译：神经外科手术中的一大挑战是区分健康组织和癌性组织，但是目前尚无合适的无创实时成像技术。光学相干断层扫描（OCT）是用于这种模态的潜在技术。 OCT的穿透深度为1-2 mm，分辨率为1-15μm，足以说明健康组织与脑肿瘤之间的结构差异。因此，我们使用光谱域OCT系统（Thorlabs Callisto）研究了健康的中枢神经系统和脑膜瘤样品的灰色和白色物质。石蜡包埋后，将样品切成10μm薄片，用明场显微镜进行组织学研究后，还产生了其他OCT图像。所有样品均用苏木精和曙红染色。在所有情况下，均进行B扫描和3D图像。此外，使用我们的OCT系统的内置摄像机拍摄了调查区域的摄像机图像。为了定位，所有样品的背面都用蓝色墨水标记。健康组织和脑膜瘤样品之间的结构差异在移除后最明显。石蜡包埋后，这些差异减小。可以看到OCT面部图像和显微图像之间的相关性。为了增加对比度，应用了后处理算法。因此，我们采用了光谱OCT，模式识别算法和机器学习算法，例如k均值聚类和主成分分析。

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  • 来源
  • 会议地点 San Francisco CA(US)
  • 作者

    Marcel Lenz; Robin Krug; Hubert Welp; Kirsten Schmieder; Martin R. Hofmann;

  • 作者单位

    Photonics and Terahertz-Technology, Ruhr-University Bochum, Universitaetsstr. 150, 44801 Bochum, Germany;

    Department of Neurosurgery, University Hospital Knappschaftskrankenhaus Bochum-Langendreer, In der Schornau 23-25, 44892 Bochum, Germany;

    Faculty of Electrical and Electronic Engineering, University of Applied Sciences Georg Agricola, Herner Str. 45, 44787 Bochum;

    Department of Neurosurgery, University Hospital Knappschaftskrankenhaus Bochum-Langendreer, In der Schornau 23-25, 44892 Bochum, Germany;

    Photonics and Terahertz-Technology, Ruhr-University Bochum, Universitaetsstr. 150, 44801 Bochum, Germany;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Spectroscopic OCT; pattern recognition; neurosurgery;

    机译：光谱OCT；模式识别;神经外科;

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