Segmentation of multiple organs in non-contrast 3D abdominal CT images

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• 作者：Akinobu Shimizu (1)
  Rena Ohno (1)
  Takaya Ikegami (1)
  Hidefumi Kobatake (1)
  Shigeru Nawano (2)
  Daniel Smutek (3)
  
• 关键词：Computer ; assisted diagnosis ; Three ; dimensional image ; Computed tomography ; Segmentation of abdominal organs
• 刊名：International Journal of Computer Assisted Radiology and Surgery
• 出版年：2007
• 出版时间：December 2007
• 年：2007
• 卷：2
• 期：3-4
• 页码：135-142
• 全文大小：719KB
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• 作者单位：Akinobu Shimizu (1)
  Rena Ohno (1)
  Takaya Ikegami (1)
  Hidefumi Kobatake (1)
  Shigeru Nawano (2)
  Daniel Smutek (3)
  
  1. Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan
  2. International University of Health and Welfare, Minato-ku, Tokyo, Japan
  3. 1st Medical Faculty, Charles University, Prague, Czech Republic
  

文摘

Objective We propose a simultaneous extraction method for 12 organs from non-contrast three-dimensional abdominal CT images. Materials and methods The proposed method uses an abdominal cavity standardization process and atlas guided segmentation incorporating parameter estimation with the EM algorithm to deal with the large fluctuations in the feature distribution parameters between subjects. Segmentation is then performed using multiple level sets, which minimize the energy function that considers the hierarchy and exclusiveness between organs as well as uniformity of grey values in organs. To assess the performance of the proposed method, ten non-contrast 3D CT volumes were used. Results The accuracy of the feature distribution parameter estimation was slightly improved using the proposed EM method, resulting in better performance of the segmentation process. Nine organs out of twelve were statistically improved compared with the results without the proposed parameter estimation process. The proposed multiple level sets also boosted the performance of the segmentation by 7.2 points on average compared with the atlas guided segmentation. Nine out of twelve organs were confirmed to be statistically improved compared with the atlas guided method. Conclusion The proposed method was statistically proved to have better performance in the segmentation of 3D CT volumes.