Automatic organ segmentation on torso CT images by using content-based image retrieval
Automatic organ segmentation on torso CT images by using content-based image retrieval
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DOI：
10.1117/12.912359
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发表时间：
2012-02
期刊：
影响因子：
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通讯作者：
Xiangrong Zhou;Atsuto Watanabe;Xinxin Zhou;T. Hara;R. Yokoyama;M. Kanematsu;H. Fujita
中科院分区：
文献类型：
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作者：
Xiangrong Zhou;Atsuto Watanabe;Xinxin Zhou;T. Hara;R. Yokoyama;M. Kanematsu;H. Fujita
本文提出了一种快速和强大的分割方案，自动识别和提取躯干CT图像上的一个器官区域。与基于传统图像处理技术的经验性分割特定器官的传统算法相比，所提出的方案使用完全数据驱动的方法来实现用于分割CT图像上不同器官区域的通用解决方案。我们的方案包括三个处理步骤：基于机器学习的器官定位，基于内容的图像（参考）检索，和基于图集的器官分割技术。我们应用该计划的自动分割的心脏，肝脏，脾脏，左，右肾区域的非对比度CT图像，这仍然是传统的分割算法的困难任务。这些器官的分割结果与由医学专家手动识别的地面实况进行比较。地面实况和自动分割结果之间的Jaccard相似系数集中在心脏67%、肝脏81%、脾脏78%、左肾75%和右肾77%。我们提出的方案的实用性得到了证实。
This paper presents a fast and robust segmentation scheme that automatically identifies and extracts a massive-organ region on torso CT images. In contrast to the conventional algorithms that are designed empirically for segmenting a specific organ based on traditional image processing techniques, the proposed scheme uses a fully data-driven approach to accomplish a universal solution for segmenting the different massive-organ regions on CT images. Our scheme includes three processing steps: machine-learning-based organ localization, content-based image (reference) retrieval, and atlas-based organ segmentation techniques. We applied this scheme to automatic segmentations of heart, liver, spleen, left and right kidney regions on non-contrast CT images respectively, which are still difficult tasks for traditional segmentation algorithms. The segmentation results of these organs are compared with the ground truth that manually identified by a medical expert. The Jaccard similarity coefficient between the ground truth and automated segmentation result centered on 67% for heart, 81% for liver, 78% for spleen, 75% for left kidney, and 77% for right kidney. The usefulness of our proposed scheme was confirmed.