A smart atlas for endomicroscopy using automated video retrieval
A smart atlas for endomicroscopy using automated video retrieval
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DOI：
10.1016/j.media.2011.02.003
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发表时间：
2011-08-01
影响因子：
10.9
通讯作者：
Ayache, Nicholas
中科院分区：
文献类型：
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作者：
Andre, Barbara;Vercauteren, Tom;Ayache, Nicholas
为了支持从体内内镜下进行早期上皮癌诊断的挑战性任务，我们提出了一种基于内容的视频检索方法，该方法使用专家注释的数据库。受近年来非医学内容图像检索成功的启发，我们首先调整了标准的视觉词袋方法来处理单个内窥镜图像。提出了一种局部密集的多尺度描述，以保持适当程度的不变性，在我们的情况下，平移，平面内旋转和仿射变换的强度。由于单个图像的视场可能不足，无法进行稳健的诊断，因此我们引入了一种视频拼接技术，可以提供大视场的拼接图像。为了去除异常值，检索之后是一种几何方法，该方法捕获局部特征之间空间关系的统计描述。在图像检索的基础上，我们将重点关注高效的视频检索。我们的方法通过依赖粗配准结果来考虑不同时间拍摄的图像之间的空间重叠，从而避免了视频拼接中耗时的部分。为了评估检索结果，我们执行了一个简单的最近邻分类，并进行了留一位患者的交叉验证。从二元和多类分类的结果来看，我们的方法在统计显著性上优于几种最先进的方法。我们得到的二值分类准确率为94.2%，非常接近临床期望。(C) 2011 Elsevier B.V.版权所有
To support the challenging task of early epithelial cancer diagnosis from in vivo endomicroscopy, we propose a content-based video retrieval method that uses an expert-annotated database. Motivated by the recent successes of non-medical content-based image retrieval, we first adjust the standard Bag-of-Visual-Words method to handle single endomicroscopic images. A local dense multi-scale description is proposed to keep the proper level of invariance, in our case to translations, in-plane rotations and affine transformations of the intensities. Since single images may have an insufficient field-of-view to make a robust diagnosis, we introduce a video-mosaicing technique that provides large field-of-view mosaic images. To remove outliers, retrieval is followed by a geometrical approach that captures a statistical description of the spatial relationships between the local features. Building on image retrieval, we then focus on efficient video retrieval. Our approach avoids the time-consuming parts of the video-mosaicing by relying on coarse registration results only to account for spatial overlap between images taken at different times. To evaluate the retrieval, we perform a simple nearest neighbors classification with leave-one-patient-out cross-validation. From the results of binary and multi-class classification, we show that our approach outperforms, with statistical significance, several state-of-the art methods. We obtain a binary classification accuracy of 94.2%, which is quite close to clinical expectations. (C) 2011 Elsevier B.V. All rights reserved.