Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III
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医学图像计算和计算机辅助干预 - MICCAI 2015 - 第 18 届国际会议，德国慕尼黑，2015 年 10 月 5-9 日，会议记录，第 III 部分
DOI：
10.1007/978-3-319-24574-4_69
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Lindner C
中科院分区：
文献类型：
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作者：
Lindner C
最近的工作表明，统计模型为基础的方法，导致准确和强大的结果时，适用于分割的骨形状从射线照片。为了实现良好的性能，基于模型的匹配系统需要大量的注释，这可能是非常耗时的获得。非刚性配准可以应用于未标记的图像，以获得可以构建模型的对应关系。然而，这样的模型很少像那些通过仔细的手动注释构建的模型那样有效，并且配准的准确性很难衡量。在本文中，我们表明，少量的手动注释点可以用来指导注册，从而显着提高性能的模型匹配系统，并实现接近的结果从密集的手动注释建立的模型。手动放置这样的稀疏点比完整的密集注释耗时少得多，从而可以比以前更快地为新骨骼形状构建良好的模型。我们描述了详细的实验上不同的稀疏点的数量，并证明，手动注释少于30%的点是足够的，以创建强大的和准确的模型分割髋关节和膝关节骨的射线照片。所提出的方法包括一个非常有效的和新颖的方式，估计配准精度的情况下，地面真相。
Recent work has shown that statistical model-based methods lead to accurate and robust results when applied to the segmentation of bone shapes from radiographs. To achieve good performance, model-based matching systems require large numbers of annotations, which can be very time-consuming to obtain. Non-rigid registration can be applied to unlabelled images to obtain correspondences from which models can be built. However, such models are rarely as effective as those built from careful manual annotations, and the accuracy of the registration is hard to measure. In this paper, we show that small numbers of manually annotated points can be used to guide the registration, leading to significant improvements in performance of the resulting model matching system, and achieving results close to those of a model built from dense manual annotations. Placing such sparse points manually is much less time-consuming than a full dense annotation, allowing good models to be built for new bone shapes more quickly than before. We describe detailed experiments on varying the number of sparse points, and demonstrate that manually annotating fewer than 30% of the points is sufficient to create robust and accurate models for segmenting hip and knee bones in radiographs. The proposed method includes a very effective and novel way of estimating registration accuracy in the absence of ground truth.