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首页> 外文会议>Conference on endoscopic microscopy IX >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的猪气道壁层的自动分割：与手动分割和组织学相比

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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：的目标是开发一种自动化光学相干断层扫描（OCT）的分割方法。我们评估了三名当然活体猪呼吸道标本;从每个气道标本选择了六个非顺序OCT图像。组织学还为每个气道进行，并且组织学图像被共同配准到OCT图像进行比较。用于组织学和OCT图像进行气道管腔面积，粘膜区域，粘膜下层区域和外气道壁面积的手工分割。采用despecking滤波器用于预处理，内腔和外气道壁区域分割，和K-means聚类用于粘膜和粘膜下层区域分割基于粗麻布滤波器OCT图像的自动分割。奥特曼分析表明，有自动分段OCT和组织学测量的气道管腔面积（偏置= -6％，95％CI = -21％-8％），粘膜区域，（偏压之间存在非常小的偏置= -4 ％，95％CI = 14％-5％），粘膜下层区（偏置= 7％，95％CI = -7％-20％）和外气道壁区域分割结果（偏置= -5％，95％CI = -14％-5％）。我们还比较了自动和手动OCT分割和奥特曼分析表明，有管腔面积（偏置= 4％，95％CI = 1％-8％），粘膜区域（偏置= -3％，95％之间可忽略的偏压CI = -6％-1％），粘膜下层区（偏置= -2％，95％CI = 10％-6％）和外气道壁（偏置= -3％，95％CI = 13％ - 6％）。这里开发的OCT气道成像自动分割方法允许气道壁组分的准确和精确的分割，这表明了该方法的该翻译的体内人呼吸道分析将允许纵向和串行研究。

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