Development of a universal organ segmentation method based on similar image retrieval and machine-learning by using a large database of medical images

利用大型医学图像数据库开发基于相似图像检索和机器学习的通用器官分割方法

• 批准号: 23500118
• 负责人: ZHOU Xiangrong
• 金额: $ 3.08万
• 依托单位: Gifu University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2011
• 资助国家: 日本
• 起止时间: 2011 至 2013
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23500118/
• 关键词: データベース; 情報システム; 学習と知識獲得; 画像情報処理; 医用・生体画像; 体幹部CT画像; 臓器の自動抽出; 医用画像データベース; 機械学習; 臓器領域の自動抽出; 画像データベース; 解剖構造の理解; 類似画像検索; 国際情報交換「米国」; 解剖学的構造; 計算機支援診断

The aim of this research (one procedure solves different organ segmentation problems on medical images) was almost realized. Our research policy for procedure design by using (machine learning and data driven based approach instead of transferring the human experience to computer program directly) was proved to be efficient to solve organ segmentation problems on medical images. We reached a conclusion that (image processing procedures such as organ segmentation can be greatly simplified under the supporting of a large database and well preparations). The algorithm that proposed to (accomplish the automatic organ segmentation within several tens of seconds) was implemented. We evaluated the performance of the algorithm and confirmed that the major organ such as kidney, spleen, and so on can be segmented automatically within one minute from the torso CT images.

这项研究的目的（一个程序解决了医学图像上的不同器官分割问题）几乎已经实现。我们通过使用（基于机器学习和基于数据驱动的方法而不是直接将人类体验转移到计算机程序）的过程设计政策被证明是有效地解决医疗图像上器官细分问题的效率。我们得出的结论是（在大型数据库和井的支持下，可以大大简化诸如器官分割的图像处理程序）。实施了提出的（在几十秒内完成自动器官分割）的算法。我们评估了算法的性能，并确认肾脏，脾脏等主要器官可以在躯干CT图像的一分钟内自动分割。

项目成果

Automatic organ segmentation on torso CT images by using content-based image retrieval

• DOI: 10.1117/12.912359
• 发表时间: 2012-02
• 作者: Xiangrong Zhou;Atsuto Watanabe;Xinxin Zhou;T. Hara;R. Yokoyama;M. Kanematsu;H. Fujita

Automatic organ localizations on 3D CT images by using majority-voting of multiple 2D detections based on local binary patterns and Haar-like features

• DOI: 10.1117/12.2007466
• 发表时间: 2013-02
• 作者: Xiangrong Zhou;Shoutarou Yamaguchi;Xinxin Zhou;Huayue Chen;T. Hara;R. Yokoyama;M. Kanematsu;H. Fujita

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

基于稀疏表示改进纹理学习模型的肺气肿分类

• DOI: 10.1117/12.2007934
• 发表时间: 2013
• 期刊: Proc. of SPIE Medical Imaging 2013: Computer-Aided Diagnosis
• 作者: M.Zhang;X.Zhou;H.Chen;C.Muramatsu;T.Hara;R.Yokoyama;M.Kanematsu;and H.Fujita
• 通讯作者: and H.Fujita

Automated scheme for measuring mandibular cortical thickness on dental panoramic radiographs for osteoporosis screening

在牙科全景X光片上测量下颌骨皮质厚度以进行骨质疏松症筛查的自动化方案

• DOI: 10.1117/12.912309
• 发表时间: 2012
• 期刊: Proc.of SPIE medical imaging
• 作者: T.Matsumoto;T.Hayashi;T.Hara;A.Katsumata;C.Muramatsu;X.Zhou;Y.Iida;M.Matsuoka;K.Katagi;H.Fujita
• 通讯作者: H.Fujita

A universal approach for automatic organ segmentations on 3D CT images based on organ localization and 3D GrabCut

• DOI: 10.1117/12.2043507
• 发表时间: 2014-03
• 作者: Xiangrong Zhou;Takaaki Ito;Xinxin Zhou;Huayue Chen;T. Hara;R. Yokoyama;M. Kanematsu;H. Hoshi;H. Fujita