Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
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DOI：
10.1109/tmi.2009.2013851
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发表时间：
2009-08-01
影响因子：
10.6
通讯作者：
Wolf, Ivo
中科院分区：
文献类型：
--
作者：
Heimann, Tobias;van Ginneken, Bram;Wolf, Ivo
本文提出了一个比较研究之间的10个自动和6个交互式的方法，肝脏分割对比增强CT图像。它基于“MICCAI 2007大挑战”研讨会的结果，其中16个团队在一个公共数据库上评估了他们的算法。提供了20幅具有参考分割的临床图像的集合，以提前训练和调整算法。还允许参与者为此目的使用额外的专有培训数据。然后，所有团队都必须将他们的方法应用于10个测试数据集，并提交获得的结果。采用的算法包括统计形状模型，图集注册，水平集，图形切割和基于规则的系统。将所有结果与参考分割进行比较，五个误差测量突出了分割准确性的不同方面。根据将获得的值与人类专家变异性相关的特定评分系统，将所有测量组合。总体而言，交互式方法的平均得分高于自动方法，并且具有更好的分割质量一致性。然而，最好的自动化方法（主要基于统计形状模型和一些额外的自由变形）可以在大多数测试图像上很好地竞争。该研究提供了在现实世界条件下不同分割方法的性能的见解，并突出了当前图像分析技术的成就和局限性。
This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.