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Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images

  • Chengwen Chu
  • Masahiro Oda
  • Takayuki Kitasaka
  • Kazunari Misawa
  • Michitaka Fujiwara
  • Yuichiro Hayashi
  • Yukitaka Nimura
  • Daniel Rueckert
  • Kensaku Mori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

This paper presents an automated multi-organ segmentation method for 3D abdominal CT images based on a spatially-divided probabilistic atlases. Most previous abdominal organ segmentation methods are ineffective to deal with the large differences among patients in organ shape and position in local areas. In this paper, we propose an automated multi-organ segmentation method based on a spatially-divided probabilistic atlas, and solve this problem by introducing a scale hierarchical probabilistic atlas. The algorithm consists of image-space division and a multi-scale weighting scheme. The generated spatial-divided probabilistic atlas efficiently reduces the inter-subject variance in organ shape and position either in global or local regions. Our proposed method was evaluated using 100 abdominal CT volumes with manually traced ground truth data. Experimental results showed that it can segment the liver, spleen, pancreas, and kidneys with Dice similarity indices of 95.1%, 91.4%, 69.1%, and 90.1%, respectively.

Keywords

Target Image Segmentation Accuracy Normalize Cross Correlation Local Weight Statistical Shape Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chengwen Chu
    • 1
  • Masahiro Oda
    • 1
  • Takayuki Kitasaka
    • 2
  • Kazunari Misawa
    • 3
  • Michitaka Fujiwara
    • 4
  • Yuichiro Hayashi
    • 5
  • Yukitaka Nimura
    • 5
  • Daniel Rueckert
    • 6
  • Kensaku Mori
    • 5
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Aichi Institute of TechnologyToyotaJapan
  3. 3.Aichi Cancer Center HospitalNagoyaJapan
  4. 4.Graduate School of MedicineNagoya UniversityNagoyaJapan
  5. 5.Information and Communications HeadquartersNagoya UniversityNagoyaJapan
  6. 6.Imperial College LondonLondonUK

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