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首页> 外文会议>Endoscopic microscopy IX; and Optical Techniques in Pulmonary Medicine >Automated Segmentation of Porcine Airway Wall Layers using Optical Coherence Tomography: Comparison with Manual Segmentation and Histology
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Automated Segmentation of Porcine Airway Wall Layers using Optical Coherence Tomography: Comparison with Manual Segmentation and Histology

机译：使用光学相干断层扫描自动分割猪气道壁层：与手动分割和组织学的比较

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摘要

The objective was to develop an automated optical coherence tomography (OCT) segmentation method. We evaluated three ex-vivo porcine airway specimens; six non-sequential OCT images were selected from each airway specimen. Histology was also performed for each airway and histology images were co-registered to OCT images for comparison. Manual segmentation of the airway luminal area, mucosa area, submucosa area and the outer airway wall area were performed for histology and OCT images. Automated segmentation of OCT images employed a despecking filter for pre-processing, a hessian-based filter for lumen and outer airway wall area segmentation, and K-means clustering for mucosa and submucosa area segmentation. Bland-Altman analysis indicated that there was very little bias between automated OCT segmentation and histology measurements for the airway lumen area (bias=-6%, 95% CI=-21%-8%), mucosa area, (bias=-4%, 95% CI=-14%-5%), submucosa area (bias=7%, 95% CI=-7%-20%) and outer airway wall area segmentation results (bias=-5%, 95% CI=-14%-5%). We also compared automated and manual OCT segmentation and Bland-Altman analysis indicated that there was negligible bias between luminal area (bias=4%, 95% CI=l%-8%), mucosa area (bias=-3%, 95% CI=-6%-1%), submucosa area (bias=-2%, 95% CI=-10%-6%) and the outer airway wall (bias=-3%, 95% CI=-13%-6%). The automated segmentation method for OCT airway imaging developed here allows for accurate and precise segmentation of the airway wall components, suggesting that translation of this method to in vivo human airway analysis would allow for longitudinal and serial studies.
机译：目的是开发一种自动光学相干断层扫描（OCT）分割方法。我们评估了三个离体猪气道标本。从每个气道标本中选择六张非连续的OCT图像。还对每个气道进行组织学检查，并将组织学图像与OCT图像共同配准以进行比较。手动分割气道腔区域，粘膜区域，粘膜下区域和气道外壁区域，以进行组织学和OCT图像。 OCT图像的自动分割采用去斑点过滤器进行预处理，基于粗麻布的过滤器进行内腔和气道外壁区域分割，以及采用K均值聚类进行黏膜和黏膜下区域分割。 Bland-Altman分析表明，自动OCT分割和组织学测量值在气管腔区域（偏倚= -6％，95％CI = -21％-8％），粘膜区域（偏倚= -4）之间几乎没有偏差。 ％，95％CI = -14％-5％），粘膜下面积（偏倚= 7％，95％CI = -7％-20％）和气道外壁区域分割结果（偏倚= -5％，95％CI = -14％-5％）。我们还比较了自动和手动OCT分割，Bland-Altman分析表明，管腔面积（偏差= 4％，95％CI = 1％-8％），粘膜面积（偏差= -3％，95％）之间的偏差可忽略不计CI = -6％-1％），粘膜下面积（偏倚= -2％，95％CI = -10％-6％）和气道外壁（偏倚= -3％，95％CI = -13％- 6％）。本文开发的用于OCT气道成像的自动分割方法可对气道壁成分进行准确和精确的分割，这表明将该方法转换为体内人气道分析可进行纵向和系列研究。

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

    Miranda Kirby; Anthony M.D. Lee; Tara Candido; Calum MacAulay; Pierre Lane; Stephen Lam; Harvey O. Coxson;

  • 作者单位

    Department of Radiology, The University of British Columbia, 3350-950 West 10th Ave.,Vancouver, BC Canada V5Z 4E3,UBC James Hogg Research Centre, St. Paul's Hospital, 166-1081 Burrard St., Vancouver, BC Canada V6Z 1Y6;

    BC Cancer Research Centre, 675 West 10th Ave., Vancouver, BC Canada V5Z 1L3;

    Department of Radiology, The University of British Columbia, 3350-950 West 10th Ave.,Vancouver, BC Canada V5Z 4E3;

    BC Cancer Research Centre, 675 West 10th Ave., Vancouver, BC Canada V5Z 1L3;

    BC Cancer Research Centre, 675 West 10th Ave., Vancouver, BC Canada V5Z 1L3;

    BC Cancer Research Centre, 675 West 10th Ave., Vancouver, BC Canada V5Z 1L3;

    Department of Radiology, The University of British Columbia, 3350-950 West 10th Ave.,Vancouver, BC Canada V5Z 4E3,UBC James Hogg Research Centre, St. Paul's Hospital, 166-1081 Burrard St., Vancouver, BC Canada V6Z 1Y6;

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

    Optical Coherence Tomography; Automated Segmentation; Histology; Airway Wall;

    机译：光学相干断层扫描；自动分割；组织学气道壁;

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